Facial_Keypoints_Detection/Facial_Keypoints_detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "t3GqUX57G2Aj"
   },
   "source": [
    "# Importing Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
    },
    "colab_type": "code",
    "id": "cXItPpLKG1cQ",
    "outputId": "55cc496e-e21b-4355-f4a9-2c580ed43646"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Device: cuda:1\n",
      "PyTorch Version: 1.1.0\n"
     ]
    }
   ],
   "source": [
    "import torch \n",
    "import torch.nn as nn\n",
    "import torch.functional as F\n",
    "\n",
    "from torchvision.datasets import ImageFolder\n",
    "from torchvision import transforms\n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "import torch.optim as optim \n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "import torchvision.utils as imutils\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "from PIL import Image\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from torchsummary import summary\n",
    "\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\") # Using CUDA or CPU based on availibility\n",
    "print(\"Device: {}\".format(device))\n",
    "\n",
    "print(\"PyTorch Version: {}\".format(torch.__version__)) # PyTorch Version"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nM8NK9XIHJzR"
   },
   "source": [
    "# Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "x8spR5CYHCpx",
    "outputId": "6ed3b09e-6d03-4b7f-d867-6e5801661086"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "BASE_DIR = '.'\n",
    "print(os.path.exists(BASE_DIR))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Uq5hzvByauru"
   },
   "source": [
    "## Visualization Utility Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yIrrCBqrayVW"
   },
   "outputs": [],
   "source": [
    "# Function to show face image along with 15 Facial Keypoints\n",
    "def show_landmarks(image,landmarks,actual_landmarks=None,figsize=(5,5)):\n",
    "    #landmarks-> actual landmarks should be passed when only one true landmark is to be shown otherwise predicted landmark.\n",
    "    #actual_landmarks -> when actual and predicted landmarks need to be compared\n",
    "    '''\n",
    "    This function assumes that the image is a grayscale and 2D(no channel) & is passed as pytorch tensor.\n",
    "    It also assumes that the image is normalized with mean = 0.5 and standard deviation=0.5\n",
    "    \n",
    "    '''\n",
    "  plt.figure(figsize=figsize)\n",
    "  img = image.numpy().squeeze()*0.5+0.5\n",
    "  plt.imshow(img,cmap='gray')\n",
    "  ind_even = [i for i in range(0,30,2)]\n",
    "  ind_odd = [i for i in range(1,30,2)]\n",
    "  plt.scatter(landmarks[ind_even].numpy(),landmarks[ind_odd].numpy(),c='r')\n",
    "  if actual_landmarks is not None:\n",
    "    plt.scatter(actual_landmarks[ind_even].numpy(),actual_landmarks[ind_odd].numpy(),c='y')\n",
    "    plt.legend(['Predicted','Actual'])\n",
    "  plt.axis('off')\n",
    "  plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "IchTzbMWKJD5"
   },
   "source": [
    "## Custom Dataset Class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 15 keypoints, which represent the following elements of the face:\n",
    "\n",
    "left_eye_center, right_eye_center, left_eye_inner_corner, left_eye_outer_corner, right_eye_inner_corner, right_eye_outer_corner, left_eyebrow_inner_end, left_eyebrow_outer_end, right_eyebrow_inner_end, right_eyebrow_outer_end, nose_tip, mouth_left_corner, mouth_right_corner, mouth_center_top_lip, mouth_center_bottom_lip\n",
    "\n",
    "source: https://www.kaggle.com/c/facial-keypoints-detection/data\n",
    "\n",
    "\n",
    "1. **training.csv**: list of training 7049 images. Each row contains the (x,y) coordinates for 15 keypoints, and image data as row-ordered list of pixels.\n",
    "2. **test.csv**: list of 1783 test images. Each row contains ImageId and image data as row-ordered list of pixels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lqBfCKBQJ0jO"
   },
   "outputs": [],
   "source": [
    "# Custom Dataset Class for Facial Keypoints Dataset\n",
    "class FaceDataset(Dataset):\n",
    "  def __init__(self, root,csv_file,dimension,transform=None,train=False,train_size=0.8,test_size=0.2):\n",
    "    self.data = pd.read_csv(os.path.join(root, csv_file)) # Reads the training.csv file as pandas Dataframe\n",
    "    self.data = self.data.dropna() # It drops all the rows having NULL values as data cleaning\n",
    "    self.X = self.data.drop([self.data.columns.tolist()[-1]],axis=1) # List of Keypoints locations\n",
    "    self.Y = self.data[self.data.columns.tolist()[-1]] # Pixel values of images\n",
    "    self.dimension = dimension\n",
    "    self.is_train = train\n",
    "    \n",
    "    # It divides the dataset based on the train_size passed\n",
    "    if train:\n",
    "      self.X,_,self.Y,_ = train_test_split(self.X,self.Y,train_size = train_size, random_state=40) \n",
    "    else:\n",
    "      _,self.X,_,self.Y = train_test_split(self.X,self.Y,test_size = test_size, random_state=40)\n",
    "    \n",
    "    self.length = len(self.X)\n",
    "    self.transform = transform\n",
    "\n",
    "  def __len__(self):\n",
    "    return self.length\n",
    "  \n",
    "  def __getitem__(self,idx):\n",
    "    if torch.is_tensor(idx):\n",
    "      idx = idx.tolist()\n",
    "    \n",
    "    '''\n",
    "    30 coordinates (15  x-y pairs) are extracted from idx-th row from the train/test split data (X).\n",
    "    It is stored as 1D Tensor and 30D Vector.\n",
    "    The order of coordinates are:\n",
    "    \n",
    "    left_eye_center_x, left_eye_center_y, right_eye_center_x, right_eye_center_y, left_eye_inner_corner_x, \n",
    "    left_eye_inner_corner_y, left_eye_outer_corner_x, left_eye_outer_corner_y, right_eye_inner_corner_x, \n",
    "    right_eye_inner_corner_y, right_eye_outer_corner_x, right_eye_outer_corner_y, left_eyebrow_inner_end_x, \n",
    "    left_eyebrow_inner_end_y, left_eyebrow_outer_end_x, left_eyebrow_outer_end_y, right_eyebrow_inner_end_x, \n",
    "    right_eyebrow_inner_end_y, right_eyebrow_outer_end_x, right_eyebrow_outer_end_y, nose_tip_x, nose_tip_y, \n",
    "    mouth_left_corner_x, mouth_left_corner_y, mouth_right_corner_x, mouth_right_corner_y, mouth_center_top_lip_x, \n",
    "    mouth_center_top_lip_y, mouth_center_bottom_lip_x, mouth_center_bottom_lip_y\n",
    "    '''\n",
    "    keypoints = torch.from_numpy(self.X.iloc[idx,:].values.astype('float64'))\n",
    "    \n",
    "    '''\n",
    "    Image pixels from idx-th row of Y is converted to float and the reshaped to given dimension,\n",
    "    which in this case is 96x96.\n",
    "    '''\n",
    "    img = list(map(float,self.Y.iloc[idx].strip().split()))\n",
    "    img = np.array(img)\n",
    "    img = np.reshape(img,self.dimension)\n",
    "    '''\n",
    "    After reshaping, it is converted to PIL Image so that torchvision can work upon it.\n",
    "    Image is the then transformed using the transforms provided as parameter is constructor\n",
    "    '''\n",
    "    img = Image.fromarray(img)\n",
    "\n",
    "    if self.transform:\n",
    "      img = self.transform(img)\n",
    "    \n",
    "    return img, keypoints"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Hf7XPvhlc2dK"
   },
   "source": [
    "## Visualizing Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "gd9yna3wT5WG"
   },
   "outputs": [],
   "source": [
    "# Transforms to apply to images\n",
    "transform = transforms.Compose([\n",
    "  transforms.ToTensor(), # converts the PIL Image to PyTorch Tensor\n",
    "  transforms.Normalize(mean=[0.5],std=[0.5]) # Image is normalized with mean=0.5 and std=0.5\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aoCcn3wsTral"
   },
   "outputs": [],
   "source": [
    "dataset = FaceDataset(BASE_DIR,'training.csv',dimension=(96,96),transform = transform, train=True) # Training Data\n",
    "test_dataset = FaceDataset(BASE_DIR,'training.csv',dimension=(96,96),transform = transform, train=False) # Test/Validation Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 303
    },
    "colab_type": "code",
    "id": "ECIDrdlFWLbW",
    "outputId": "cd253ced-36db-48f9-bb1f-a1abe0d913f2"
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Random Image from Train Data\n",
    "ind = np.random.randint(0,len(dataset))\n",
    "show_landmarks(dataset[ind][0],dataset[ind][1],actual_landmarks=None, figsize=(5,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 303
    },
    "colab_type": "code",
    "id": "Kgc-Rfs5dL4S",
    "outputId": "5345d67f-32b1-40db-9f78-160fb63cc599"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Random Image from Test Data\n",
    "ind = np.random.randint(0,len(test_dataset))\n",
    "show_landmarks(test_dataset[ind][0],test_dataset[ind][1],actual_landmarks=None, figsize=(5,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tRhaDlNhd56u"
   },
   "source": [
    "# Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SWMjoWyod6wD"
   },
   "outputs": [],
   "source": [
    "# A basic CNN-based Regressor model for Facial Keypoints Prediction.\n",
    "'''\n",
    "1. Feature Extractor uses CNN architecture\n",
    "2. Features Extrated are then passed through Fully Connected Neural Network Acting as Regressor.\n",
    "3. Input Size = (1,96,96) | Output Size: (30) [15 (x,y) pairs]\n",
    "'''\n",
    "\n",
    "class Net(nn.Module):\n",
    "  def __init__(self):\n",
    "    super(Net,self).__init__()\n",
    "\n",
    "    self.features = nn.Sequential(\n",
    "        nn.Conv2d(in_channels=1, out_channels=32, kernel_size=(5,5), stride=1, padding=2),\n",
    "        nn.ReLU(),\n",
    "        nn.BatchNorm2d(32),\n",
    "        nn.MaxPool2d(2,2),\n",
    "\n",
    "        nn.Conv2d(in_channels=32, out_channels=64, kernel_size=(3,3), stride=1, padding=1),\n",
    "        nn.ReLU(),\n",
    "        nn.BatchNorm2d(64),\n",
    "        nn.MaxPool2d(2,2),\n",
    "\n",
    "        nn.Conv2d(in_channels=64, out_channels=128, kernel_size=(3,3), stride=1, padding=1),\n",
    "        nn.ReLU(),\n",
    "        nn.BatchNorm2d(128),\n",
    "        nn.MaxPool2d(2,2),\n",
    "        \n",
    "        nn.Conv2d(in_channels=128, out_channels=256, kernel_size=(3,3), stride=1, padding=1),\n",
    "        nn.ReLU(),\n",
    "        nn.BatchNorm2d(256),\n",
    "        nn.MaxPool2d(2,2),\n",
    "        nn.Conv2d(in_channels=256, out_channels=512, kernel_size=(3,3), stride=1, padding=1),\n",
    "        nn.ReLU(),\n",
    "        nn.BatchNorm2d(512)\n",
    "    )\n",
    "\n",
    "    self.regressor = nn.Sequential(\n",
    "        nn.Linear(512*6*6,256),\n",
    "        nn.LeakyReLU(0.2),\n",
    "        nn.Dropout(0.4),\n",
    "        nn.Linear(256,30)\n",
    "    ) \n",
    "    \n",
    "  def forward(self, input_):\n",
    "    input_ = self.features(input_)\n",
    "    input_ = input_.view(input_.size(0),-1)\n",
    "    return self.regressor(input_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 641
    },
    "colab_type": "code",
    "id": "OOAaJLa_fIfS",
    "outputId": "d6d6a824-e317-47e1-e3a8-7b643608476e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Conv2d-1           [-1, 32, 96, 96]             832\n",
      "              ReLU-2           [-1, 32, 96, 96]               0\n",
      "       BatchNorm2d-3           [-1, 32, 96, 96]              64\n",
      "         MaxPool2d-4           [-1, 32, 48, 48]               0\n",
      "            Conv2d-5           [-1, 64, 48, 48]          18,496\n",
      "              ReLU-6           [-1, 64, 48, 48]               0\n",
      "       BatchNorm2d-7           [-1, 64, 48, 48]             128\n",
      "         MaxPool2d-8           [-1, 64, 24, 24]               0\n",
      "            Conv2d-9          [-1, 128, 24, 24]          73,856\n",
      "             ReLU-10          [-1, 128, 24, 24]               0\n",
      "      BatchNorm2d-11          [-1, 128, 24, 24]             256\n",
      "        MaxPool2d-12          [-1, 128, 12, 12]               0\n",
      "           Conv2d-13          [-1, 256, 12, 12]         295,168\n",
      "             ReLU-14          [-1, 256, 12, 12]               0\n",
      "      BatchNorm2d-15          [-1, 256, 12, 12]             512\n",
      "        MaxPool2d-16            [-1, 256, 6, 6]               0\n",
      "           Conv2d-17            [-1, 512, 6, 6]       1,180,160\n",
      "             ReLU-18            [-1, 512, 6, 6]               0\n",
      "      BatchNorm2d-19            [-1, 512, 6, 6]           1,024\n",
      "           Linear-20                  [-1, 256]       4,718,848\n",
      "        LeakyReLU-21                  [-1, 256]               0\n",
      "          Dropout-22                  [-1, 256]               0\n",
      "           Linear-23                   [-1, 30]           7,710\n",
      "================================================================\n",
      "Total params: 6,297,054\n",
      "Trainable params: 6,297,054\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 0.04\n",
      "Forward/backward pass size (MB): 14.14\n",
      "Params size (MB): 24.02\n",
      "Estimated Total Size (MB): 38.20\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "model = Net()\n",
    "summary(model,(1,96,96),device=\"cpu\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZAYJeg5PnonZ"
   },
   "source": [
    "# Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0jS4nzwXn4jp"
   },
   "outputs": [],
   "source": [
    "trainLoader = DataLoader(dataset, batch_size = 64, shuffle=True, num_workers=20) # Batch generator for training Data\n",
    "testLoader = DataLoader(test_dataset,batch_size=64,shuffle=True, num_workers=20) # Batch Generator for test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WkBflx6fmOnr"
   },
   "outputs": [],
   "source": [
    "model = Net() # Instantiating the model\n",
    "loss_fn = nn.MSELoss() # Choosing the Loss Function as Mean Squared Error since the task is regression\n",
    "model = model.to(device) # Moving the model to device ('cuda' or 'cpu')\n",
    "optimizer = optim.Adam(model.parameters(),lr=0.0001) # Choosing Adam optimizer with learning_rate-1e-4. Assigning model's\n",
    "                                                    # weight to it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
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      "Epoch: 48/60 loss:0.06025702332224801 |  test_loss:0.07541471552625995 | \n"
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     "output_type": "stream",
     "text": [
      "Epoch: 49/60 loss:0.05432249522097757 |  test_loss:0.06762144721557047 | \n"
     ]
    },
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 50/60 loss:0.061838826425721714 |  test_loss:0.07619609955315278 | \n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "Epoch: 51/60 loss:0.05667825182464635 |  test_loss:0.07056189196132054 | \n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "Epoch: 52/60 loss:0.06061270805162804 |  test_loss:0.07812094966941904 | \n"
     ]
    },
    {
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     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 53/60 loss:0.05620782996449515 |  test_loss:0.0726431290680003 | \n"
     ]
    },
    {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 54/60 loss:0.05845443027041783 |  test_loss:0.07516884191013942 | \n"
     ]
    },
    {
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       "HBox(children=(IntProgress(value=0, max=27), HTML(value='')))"
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     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 55/60 loss:0.055001554505847323 |  test_loss:0.0692162329905501 | \n"
     ]
    },
    {
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       "HBox(children=(IntProgress(value=0, max=27), HTML(value='')))"
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     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 56/60 loss:0.05410907675172681 |  test_loss:0.06853291865821197 | \n"
     ]
    },
    {
     "data": {
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       "HBox(children=(IntProgress(value=0, max=27), HTML(value='')))"
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     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 57/60 loss:0.0563186065894421 |  test_loss:0.07091281402890927 | \n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=27), HTML(value='')))"
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     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 58/60 loss:0.05480545606011542 |  test_loss:0.0699612587411827 | \n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "189c5bce12ee4facad2fe42e52636e6e",
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       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=27), HTML(value='')))"
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     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 59/60 loss:0.05154603441184926 |  test_loss:0.06405257733068734 | \n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "80e4858916a24773acff34c991e8e1ed",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=27), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 60/60 loss:0.051159172275356046 |  test_loss:0.064430237373459 | \n"
     ]
    }
   ],
   "source": [
    "num_epochs = 60\n",
    "# metrics to store per epoch Loss for Training and Testing Data\n",
    "metrics = {\n",
    "    \"loss\":{'train':[],'test':[]},\n",
    "}\n",
    "\n",
    "for epoch in range(1,num_epochs+1):\n",
    "    # metrics_temp used to calculate metrics for every individual epochs\n",
    "  metrics_temp = {\n",
    "    \"loss\":{'train':0,'test':0},\n",
    "    \"count\":{'train':0,'test':0},\n",
    "  }\n",
    "  lr = 0.0001 * (0.5 ** (epoch // 15))\n",
    "  for param_group in optimizer.param_groups:\n",
    "      param_group['lr'] = lr\n",
    "    \n",
    "  # Training the data\n",
    "\n",
    "  # Putting the model in train mode so that DropOut works.  \n",
    "  model.train()\n",
    "  for i, data in tqdm(enumerate(trainLoader,0),total=len(trainLoader),unit='Step'):\n",
    "    X,Y = data # Splitting data into input,target\n",
    "    X,Y = X.to(device),Y.to(device) # Moving input and target to GPU\n",
    "    output = model(X) # passing the input to model and Output is calculated for every image in batch\n",
    "    loss = loss_fn(output,Y.float()) # loss is calcuated between predicted(output) and target(Y)\n",
    "    model.zero_grad() # Setting the gradient to zero for the model\n",
    "    optimizer.zero_grad()# Setting the gradient to zero for optimizer\n",
    "    loss.backward()# Calculating gradient for every tensor which is used to calculate 'loss'\n",
    "    optimizer.step()# Weights(parameters) are updated which are related to 'optimizer' i.e., model.classifier\n",
    "    del X,Y,loss# X,Y and loss are deleted for saving memory\n",
    "\n",
    "  # Evaluating the model for this epoch\n",
    "\n",
    "  # Putting the model in evaluation mode which turns of Dropout and parameters are fixed for every model.\n",
    "  model.eval()\n",
    "  with torch.no_grad():\n",
    "    #Train Data\n",
    "    for i, data in enumerate(trainLoader,0):\n",
    "      X,Y = data\n",
    "      X,Y = X.to(device),Y.to(device)\n",
    "      # Y = Y.unsqueeze(1)\n",
    "      output = model(X)\n",
    "      loss = loss_fn(output,Y.float())\n",
    "      metrics_temp['loss']['train']+=loss.item()\n",
    "      metrics_temp['count']['train']+=len(Y)\n",
    "      del X,Y,loss\n",
    "\n",
    "    #Test Data\n",
    "    for i, data in enumerate(testLoader,0):\n",
    "      X,Y = data\n",
    "      X,Y = X.to(device),Y.to(device)\n",
    "      # Y = Y.unsqueeze(1)\n",
    "      output = model(X)\n",
    "      loss = loss_fn(output,Y.float())\n",
    "      metrics_temp['loss']['test']+=loss.item()\n",
    "      metrics_temp['count']['test']+=len(Y)\n",
    "      del X,Y,loss\n",
    "\n",
    "  metrics['loss']['train'].append(metrics_temp['loss']['train']/metrics_temp['count']['train'])\n",
    "\n",
    "  metrics['loss']['test'].append(metrics_temp['loss']['test']/metrics_temp['count']['test'])\n",
    "\n",
    "  print(\"Epoch: {}/{} loss:{} |  test_loss:{} | \".format(\n",
    "      epoch,num_epochs,metrics['loss']['train'][-1],metrics['loss']['test'][-1]\n",
    "  ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "F6-HULe7rPub"
   },
   "outputs": [],
   "source": [
    "torch.save(model.state_dict(),os.path.join(BASE_DIR,'weights.pth')) # Saving the weights of the trained model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6TzfcHtGpoHS"
   },
   "source": [
    "# Metrics Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 281
    },
    "colab_type": "code",
    "id": "zW7bpzfWpnka",
    "outputId": "c66ac116-c6ed-4889-d57e-8bfb1c9489bf"
   },
   "outputs": [
    {
     "data": {
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/DLxoZufWH3o7sKFpFWWz9Y66adPgIiJtZbx7ffwB8KX6Hh8/Bz7YtIrMiKpGXFNQi4jA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\nYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Loss Plot\n",
    "plt.plot(metrics['loss']['train'],'r')\n",
    "plt.plot(metrics['loss']['test'],'g')\n",
    "plt.legend(['training','testing'])\n",
    "plt.title('Loss Plot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-vTw8TjlqDHD"
   },
   "source": [
    "# Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "IYkVyw9hSa78",
    "outputId": "3ade6603-de44-4904-a424-8b42536bd639"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IncompatibleKeys(missing_keys=[], unexpected_keys=[])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.load_state_dict(torch.load(os.path.join(BASE_DIR,'weights.pth'))) # Loading the saved weights of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 303
    },
    "colab_type": "code",
    "id": "IhvjBtPup_s9",
    "outputId": "ac45df51-47ea-4cbf-e010-6c7d426be452"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Testing on different test images\n",
    "with torch.no_grad():\n",
    "  ind = np.random.randint(0,len(test_dataset))\n",
    "  img = test_dataset[ind][0].unsqueeze(0)\n",
    "  model.cpu()\n",
    "  model.eval()\n",
    "  pred_landmarks = model(img)\n",
    "  # print(pred_landmarks)\n",
    "  show_landmarks(img,pred_landmarks.squeeze(),test_dataset[ind][1],figsize=(5,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "OfDrvW30uscz"
   },
   "source": [
    "# Kaggle Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0C43TLkByT65"
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv(os.path.join(BASE_DIR,'training.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "F3PjCJlAyZOf"
   },
   "outputs": [],
   "source": [
    "columns = data.columns.tolist()[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "left_eye_center_x, left_eye_center_y, right_eye_center_x, right_eye_center_y, left_eye_inner_corner_x, left_eye_inner_corner_y, left_eye_outer_corner_x, left_eye_outer_corner_y, right_eye_inner_corner_x, right_eye_inner_corner_y, right_eye_outer_corner_x, right_eye_outer_corner_y, left_eyebrow_inner_end_x, left_eyebrow_inner_end_y, left_eyebrow_outer_end_x, left_eyebrow_outer_end_y, right_eyebrow_inner_end_x, right_eyebrow_inner_end_y, right_eyebrow_outer_end_x, right_eyebrow_outer_end_y, nose_tip_x, nose_tip_y, mouth_left_corner_x, mouth_left_corner_y, mouth_right_corner_x, mouth_right_corner_y, mouth_center_top_lip_x, mouth_center_top_lip_y, mouth_center_bottom_lip_x, mouth_center_bottom_lip_y, "
     ]
    }
   ],
   "source": [
    "for column in columns:\n",
    "    print(column, end=', ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "leOWewNU1xGB"
   },
   "outputs": [],
   "source": [
    "columns_int = {}\n",
    "for i,column in enumerate(columns):\n",
    "  columns_int[column]=i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Fw0vzD2HuuVJ"
   },
   "outputs": [],
   "source": [
    "class FaceDataset_t(Dataset):\n",
    "  def __init__(self, root,csv_file,dimension,transform=None):\n",
    "    self.data = pd.read_csv(os.path.join(root, csv_file))\n",
    "    self.data = self.data.dropna()\n",
    "    self.length = len(self.data)\n",
    "    self.transform = transform\n",
    "    self.dimension = dimension\n",
    "  def __len__(self):\n",
    "    return self.length\n",
    "  \n",
    "  def __getitem__(self,idx):\n",
    "    if torch.is_tensor(idx):\n",
    "      idx = idx.tolist()\n",
    "    \n",
    "    imageid = self.data.iloc[idx,0]\n",
    "    img = list(map(float,self.data.iloc[idx,1].strip().split()))\n",
    "    img = np.array(img)\n",
    "    img = np.reshape(img,self.dimension)\n",
    "    img = Image.fromarray(img)\n",
    "\n",
    "    if self.transform:\n",
    "      img = self.transform(img)\n",
    "    \n",
    "    return img, imageid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "v0wIz58Xzqif"
   },
   "outputs": [],
   "source": [
    "testDataset = FaceDataset_t(BASE_DIR,'test.csv',dimension=(96,96),transform=transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "nJcW2ivVSo_M",
    "outputId": "6c217397-4f2f-43ec-9860-209be053527c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IncompatibleKeys(missing_keys=[], unexpected_keys=[])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.load_state_dict(torch.load(os.path.join(BASE_DIR,'weights.pth')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 303
    },
    "colab_type": "code",
    "id": "0NE_0MtqRghg",
    "outputId": "f0c0c230-572e-42d2-dc7c-2b397963e0a8"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Testing on images of test.csv\n",
    "with torch.no_grad():\n",
    "  ind = np.random.randint(0,len(testDataset))\n",
    "  img = testDataset[ind][0].unsqueeze(0)\n",
    "  model.cpu()\n",
    "  model.eval()\n",
    "  pred_landmarks = model(img)\n",
    "  # print(pred_landmarks)\n",
    "  show_landmarks(img,pred_landmarks.squeeze(),None,figsize=(5,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 133,
     "referenced_widgets": [
      "a09af45a1a98445ba6a74691ed292c9b",
      "956971541b904fd493f029e4946b547a",
      "e0511230c7994657a18012a2c303924a",
      "4b843bd48f6f4550acbac27b583d68b0",
      "eb88ed0ea6ef4286acc0e50e61de7161",
      "3f85ed8fd39e437081181fc57c39cb22",
      "71e2e3f5e2874fc996f0f89a1a85b1b5",
      "1ef245b3cc384d4fbb5cca023462bfb4",
      "c04d97f5456c44abba8535c9b70cdea6",
      "67fb148c837941a6bbc9e2c571f728d5",
      "1454dd634e224eef8d5949eb9468440f",
      "442ccfe0f9a54f619d6ee4322c2e8e54",
      "27fc84e55da54b7d9078fde4191257b6",
      "4762ea55b8a14053b7f05a59f0cb8aa6",
      "3e5d2884e7fd4a7f94b10d36f88253a5",
      "8d4d7c9a81904c2da2b75db1a36bd1e6"
     ]
    },
    "colab_type": "code",
    "id": "6HMJnFst0bi2",
    "outputId": "741deb55-8781-4b16-8f38-3aebfe0fa5a1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicting: \n"
     ]
    },
    {
     "data": {
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    "  model.eval()\n",
    "  model.cpu()\n",
    "  z = 1\n",
    "  predictions = []\n",
    "  print(\"Predicting: \")\n",
    "  for i,data in tqdm(enumerate(testDataset,1),total=len(testDataset)):\n",
    "    img = data[0].unsqueeze(0)\n",
    "    pred_landmarks = model(img)\n",
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    "    predictions.append(pred_landmarks)\n",
    "\n",
    "dd = pd.read_csv(os.path.join(BASE_DIR,'IdLookupTable.csv'))\n",
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    "  r = row[1]\n",
    "  rowId.append(r['RowId'])\n",
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    "\n",
    "output = pd.DataFrame({'RowId':rowId,'Location':Location},index=None)\n",
    "file_Name = 'submission.csv'\n",
    "output.to_csv(os.path.join(BASE_DIR,file_Name),index=False)\n",
    "print(\"Submission File save as {}\".format(file_Name))"
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