ImageAutoEncoder.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "850de471",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "3\n",
      "torch.Size([5, 3])\n",
      "tensor([ 1.8063, -0.1446,  0.4808, -1.3045,  0.3946, -0.6175, -0.3307, -0.5379,\n",
      "         0.7020,  0.7159,  0.2873, -1.1844,  1.1351, -0.3666,  0.9283])\n",
      "tensor([[ 1.8063, -0.1446,  0.4808],\n",
      "        [-1.3045,  0.3946, -0.6175],\n",
      "        [-0.3307, -0.5379,  0.7020],\n",
      "        [ 0.7159,  0.2873, -1.1844],\n",
      "        [ 1.1351, -0.3666,  0.9283]])\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision import transforms\n",
    "\n",
    "import torch\n",
    "\n",
    "a = torch.randn(5,3)\n",
    "\n",
    "b = torch.flatten(a)\n",
    "\n",
    "c = b.view(a.shape[0] , a.shape[1])\n",
    "\n",
    "print(a.shape[0])\n",
    "print(a.shape[1])\n",
    "print(a.shape)\n",
    "print(b)\n",
    "\n",
    "print(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d4b358a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os \n",
    "\n",
    "path = \"./dataset/images/\"\n",
    "image_lis = os.listdir(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "400e32c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_size = 224\n",
    "data_transforms = transforms.Compose([\n",
    "        transforms.RandomResizedCrop(input_size),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
    "    ])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a2fa7151",
   "metadata": {},
   "outputs": [],
   "source": [
    "class ImageAutoEncoder(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(ImageAutoEncoder, self).__init__()\n",
    "        \n",
    "        #Encoder\n",
    "        self.conv1 = nn.Conv2d(3, 16, 3, padding=1)  \n",
    "        self.conv2 = nn.Conv2d(16, 4, 3, padding=1)\n",
    "        self.pool = nn.MaxPool2d(2, 2)\n",
    "        \n",
    "        self.linear_1 = nn.Linear(12544 , 1200)\n",
    "        self.linear_2 = nn.Linear(1200 , 200)\n",
    "        \n",
    "        self.delinear_1 = nn.Linear(200 , 1200)\n",
    "        self.delinear_2 = nn.Linear(1200 , 12544)\n",
    "        \n",
    "        #Decoder\n",
    "        #self.t_conv1 = nn.ConvTranspose2d(4, 16, 2, stride=2)\n",
    "        #self.t_conv2 = nn.ConvTranspose2d(16, 3, 2, stride=2)\n",
    "        \n",
    "        self.deconv1 = nn.ConvTranspose2d(1, 16, 3, stride=2, output_padding=1)\n",
    "        self.deconv2 = nn.ConvTranspose2d(16, 3, 3, stride=1, padding = 2, output_padding=0)\n",
    "        \n",
    "    def forward(self , x):\n",
    "        x = F.relu(self.conv1(x))\n",
    "        x = self.pool(x)\n",
    "        x = F.relu(self.conv2(x))\n",
    "        x = self.pool(x)\n",
    "        \n",
    "        x = torch.flatten(x ,start_dim = 1)\n",
    "        x = nn.ReLU()(self.linear_1(x))\n",
    "        x = nn.ReLU()(self.linear_2(x))\n",
    "        \n",
    "        #start of decoder\n",
    "        x = nn.ReLU()(self.delinear_1(x))\n",
    "        x = nn.ReLU()(self.delinear_2(x))\n",
    "        x = x.view(-1 , 1 , 112 , 112)\n",
    "        x = nn.ReLU()(self.deconv1(x))\n",
    "        x = self.deconv2(x)\n",
    "        \n",
    "        \n",
    "        #print(\"X.shape\" , x.shape)\n",
    "        \n",
    "        return x\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "95367fde",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df_csv = pd.read_csv(\"./dataset/books_with_genres.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "06f1e7c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['isbn', 'text_reviews_count', 'series', 'country_code', 'language_code',\n",
       "       'popular_shelves', 'asin', 'is_ebook', 'average_rating', 'kindle_asin',\n",
       "       'similar_books', 'description', 'format', 'link', 'authors',\n",
       "       'publisher', 'num_pages', 'publication_day', 'isbn13',\n",
       "       'publication_month', 'edition_information', 'publication_year', 'url',\n",
       "       'image_url', 'book_id', 'ratings_count', 'work_id', 'title',\n",
       "       'title_without_series', 'history, historical fiction, biography',\n",
       "       'fiction', 'fantasy, paranormal', 'mystery, thriller, crime', 'poetry',\n",
       "       'romance', 'non-fiction', 'children', 'young-adult', 'comics, graphic',\n",
       "       'category', 'binary_category'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_csv.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c68cf9ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_csv[\"fiction_binary\"] = np.where(df_csv['binary_category']=='fiction', 1, 0)\n",
    "y = df_csv['fiction_binary']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dbf4b39d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from PIL import Image\n",
    "ind = 0\n",
    "batch_size = 32\n",
    "book_id , category = df_csv['book_id'] , df_csv['fiction_binary']\n",
    "input_list = []\n",
    "for i in book_id:\n",
    "    im = Image.open(path + str(i))\n",
    "    im_rgb = im.convert(\"RGB\")\n",
    "    input_tensor = data_transforms(im_rgb)\n",
    "    input_list.append(input_tensor)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "408ce684",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "53306eb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_X = torch.stack(input_list).cuda()\n",
    "input_Y = torch.LongTensor(y).cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "02e6c077",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([5000])\n",
      "torch.Size([5000, 3, 224, 224])\n"
     ]
    }
   ],
   "source": [
    "print(input_Y.shape)\n",
    "print(input_X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "63b55fac",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = ImageAutoEncoder().cuda()\n",
    "criterion = nn.MSELoss()\n",
    "\n",
    "optimizer = torch.optim.Adam(model.parameters() , lr = 0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "83b6b10f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration number  1\n",
      "Loss is  tensor(213.8831, device='cuda:0', grad_fn=<AddBackward0>)\n",
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      "Loss is  tensor(75.5356, device='cuda:0', grad_fn=<AddBackward0>)\n",
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      "Loss is  tensor(74.7900, device='cuda:0', grad_fn=<AddBackward0>)\n",
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      "Loss is  tensor(73.6024, device='cuda:0', grad_fn=<AddBackward0>)\n",
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      "Loss is  tensor(72.7702, device='cuda:0', grad_fn=<AddBackward0>)\n",
      "iteration number  95\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss is  tensor(72.5551, device='cuda:0', grad_fn=<AddBackward0>)\n",
      "iteration number  96\n",
      "Loss is  tensor(72.7032, device='cuda:0', grad_fn=<AddBackward0>)\n",
      "iteration number  97\n",
      "Loss is  tensor(73.0023, device='cuda:0', grad_fn=<AddBackward0>)\n",
      "iteration number  98\n",
      "Loss is  tensor(73.2439, device='cuda:0', grad_fn=<AddBackward0>)\n",
      "iteration number  99\n",
      "Loss is  tensor(73.1914, device='cuda:0', grad_fn=<AddBackward0>)\n",
      "iteration number  100\n",
      "Loss is  tensor(73.0806, device='cuda:0', grad_fn=<AddBackward0>)\n"
     ]
    }
   ],
   "source": [
    "epoch_count = 100\n",
    "batch_size = 32\n",
    "for i in range(0 , epoch_count):\n",
    "    print(\"iteration number \" , i + 1)\n",
    "    train_loss = 0\n",
    "    for j in range(0 , len(input_X) , 32):\n",
    "        img = input_X[j:j+32]#.unsqueeze(0)\n",
    "        #print(img.shape)\n",
    "        img.requires_grad_ = False\n",
    "        X_predicted = model.forward(img)\n",
    "        loss = criterion(X_predicted , img)\n",
    "        train_loss += loss\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "    print(\"Loss is \" ,train_loss)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "65a8870b",
   "metadata": {},
   "outputs": [],
   "source": [
    "use_cuda = torch.cuda.is_available()\n",
    "device = torch.device('cuda:0' if use_cuda else 'cpu')\n",
    "\n",
    "def get_embeddings(x):\n",
    "    \n",
    "      #Encoder\n",
    "    x = F.relu(model.conv1(x))\n",
    "    x = model.pool(x)\n",
    "    x = F.relu(model.conv2(x))\n",
    "    x = model.pool(x)\n",
    "\n",
    "    x = torch.flatten(x ,start_dim = 1)\n",
    "    x = nn.ReLU()(model.linear_1(x))\n",
    "    x = nn.ReLU()(model.linear_2(x))\n",
    "    \n",
    "    return x\n",
    "\n",
    "embeddings = []\n",
    "with torch.no_grad():\n",
    "    for i in book_id:\n",
    "        im = Image.open(path + str(i))\n",
    "        im_rgb = im.convert(\"RGB\")\n",
    "        input_tensor = data_transforms(im_rgb)\n",
    "        input_tensor = input_tensor.to(device)\n",
    "        val = get_embeddings(input_tensor.unsqueeze(0))\n",
    "        embeddings.append({str(i) : val})\n",
    "        \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9ab0e950",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5000\n"
     ]
    }
   ],
   "source": [
    "print(len(embeddings))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b79a2375",
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(embeddings, \"embeddings_conv_encoder_decoder.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb54f551",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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