{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "latex_metadata": {
     "author": "Aditya Mishra",
     "title": "Visual Transformer | Cats&Dogs Edition",
     "twitter": "@adi_myth"
    }
   },
   "source": [
    "# Visual Transformer with Linformer\n",
    "\n",
    "Training Visual Transformer on *Dogs vs Cats Data*\n",
    "\n",
    "* Dogs vs. Cats Redux: Kernels Edition - https://www.kaggle.com/c/dogs-vs-cats-redux-kernels-edition\n",
    "* Base Code - https://www.kaggle.com/reukki/pytorch-cnn-tutorial-with-cats-and-dogs/\n",
    "* Efficient Attention Implementation - https://github.com/lucidrains/vit-pytorch#efficient-attention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "s_z7TuN_RIOK"
   },
   "outputs": [],
   "source": [
    "!pip -q install vit_pytorch linformer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IDT1Xwv9RZXb"
   },
   "source": [
    "## Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "qq-e52uORIVg"
   },
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "\n",
    "import glob\n",
    "from itertools import chain\n",
    "import os\n",
    "import random\n",
    "import zipfile\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from linformer import Linformer\n",
    "from PIL import Image\n",
    "from sklearn.model_selection import train_test_split\n",
    "from torch.optim.lr_scheduler import StepLR\n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "from torchvision import datasets, transforms\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "from vit_pytorch.efficient import ViT\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "pV-E0IFHRITw",
    "outputId": "f4221b45-a2bf-479f-ecdb-8df9e836312a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Torch: 1.6.0+cu101\n"
     ]
    }
   ],
   "source": [
    "print(f\"Torch: {torch.__version__}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "GEoNmg0aQVf9"
   },
   "outputs": [],
   "source": [
    "# Training settings\n",
    "batch_size = 64\n",
    "epochs = 20\n",
    "lr = 3e-5\n",
    "gamma = 0.7\n",
    "seed = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "mSO-6jwwlYzO"
   },
   "outputs": [],
   "source": [
    "def seed_everything(seed):\n",
    "    random.seed(seed)\n",
    "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
    "    np.random.seed(seed)\n",
    "    torch.manual_seed(seed)\n",
    "    torch.cuda.manual_seed(seed)\n",
    "    torch.cuda.manual_seed_all(seed)\n",
    "    torch.backends.cudnn.deterministic = True\n",
    "\n",
    "seed_everything(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "ZS5Jp9SBRgQr"
   },
   "outputs": [],
   "source": [
    "device = 'cuda'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZEIyZaYXRkZF"
   },
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "gj-ZY6eVRgZW"
   },
   "outputs": [],
   "source": [
    "os.makedirs('data', exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "lvw64WGURgxK"
   },
   "outputs": [],
   "source": [
    "train_dir = 'data/train'\n",
    "test_dir = 'data/test'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "1hE3h70ZRg2g"
   },
   "outputs": [],
   "source": [
    "with zipfile.ZipFile('train.zip') as train_zip:\n",
    "    train_zip.extractall('data')\n",
    "    \n",
    "with zipfile.ZipFile('test.zip') as test_zip:\n",
    "    test_zip.extractall('data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "FVD2SEjTRgXh"
   },
   "outputs": [],
   "source": [
    "train_list = glob.glob(os.path.join(train_dir,'*.jpg'))\n",
    "test_list = glob.glob(os.path.join(test_dir, '*.jpg'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "id": "GwQ01kgtSExB",
    "outputId": "50a9ce9b-39d0-4360-e227-cbd155be31fb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Data: 25000\n",
      "Test Data: 12500\n"
     ]
    }
   ],
   "source": [
    "print(f\"Train Data: {len(train_list)}\")\n",
    "print(f\"Test Data: {len(test_list)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "amKHFCu_iwQZ"
   },
   "outputs": [],
   "source": [
    "labels = [path.split('/')[-1].split('.')[0] for path in train_list]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "H4NdaHt7R-zP"
   },
   "source": [
    "## Random Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 683
    },
    "id": "Fs8I30iIR6A1",
    "outputId": "4102a4bb-a66e-4234-e51d-2e282ece1e43"
   },
   "outputs": [
    {
     "data": {
      "image/png": "[encoded data, 929364 characters]\n",
      "text/plain": [
       "<Figure size 1152x864 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "random_idx = np.random.randint(1, len(train_list), size=9)\n",
    "fig, axes = plt.subplots(3, 3, figsize=(16, 12))\n",
    "\n",
    "for idx, ax in enumerate(axes.ravel()):\n",
    "    img = Image.open(train_list[idx])\n",
    "    ax.set_title(labels[idx])\n",
    "    ax.imshow(img)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UxHo4dU8SHcZ"
   },
   "source": [
    "## Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "YgrHNTpnR6Hl"
   },
   "outputs": [],
   "source": [
    "train_list, valid_list = train_test_split(train_list, \n",
    "                                          test_size=0.2,\n",
    "                                          stratify=labels,\n",
    "                                          random_state=seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "id": "UViq3x5yR5-r",
    "outputId": "9db06fb4-fb7e-4d07-b316-6619e2f6d030"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Data: 20000\n",
      "Validation Data: 5000\n",
      "Test Data: 12500\n"
     ]
    }
   ],
   "source": [
    "print(f\"Train Data: {len(train_list)}\")\n",
    "print(f\"Validation Data: {len(valid_list)}\")\n",
    "print(f\"Test Data: {len(test_list)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZhYDJXk2SRDu"
   },
   "source": [
    "## Image Augmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "YKR5RaykR58x"
   },
   "outputs": [],
   "source": [
    "train_transforms = transforms.Compose(\n",
    "    [\n",
    "        transforms.Resize((224, 224)),\n",
    "        transforms.RandomResizedCrop(224),\n",
    "        transforms.RandomHorizontalFlip(),\n",
    "        transforms.ToTensor(),\n",
    "    ]\n",
    ")\n",
    "\n",
    "val_transforms = transforms.Compose(\n",
    "    [\n",
    "        transforms.Resize(256),\n",
    "        transforms.CenterCrop(224),\n",
    "        transforms.ToTensor(),\n",
    "    ]\n",
    ")\n",
    "\n",
    "\n",
    "test_transforms = transforms.Compose(\n",
    "    [\n",
    "        transforms.Resize(256),\n",
    "        transforms.CenterCrop(224),\n",
    "        transforms.ToTensor(),\n",
    "    ]\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "b0JAXWWtSXpB"
   },
   "source": [
    "## Load Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "Ds9yt5kHR56R"
   },
   "outputs": [],
   "source": [
    "class CatsDogsDataset(Dataset):\n",
    "    def __init__(self, file_list, transform=None):\n",
    "        self.file_list = file_list\n",
    "        self.transform = transform\n",
    "\n",
    "    def __len__(self):\n",
    "        self.filelength = len(self.file_list)\n",
    "        return self.filelength\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        img_path = self.file_list[idx]\n",
    "        img = Image.open(img_path)\n",
    "        img_transformed = self.transform(img)\n",
    "\n",
    "        label = img_path.split(\"/\")[-1].split(\".\")[0]\n",
    "        label = 1 if label == \"dog\" else 0\n",
    "\n",
    "        return img_transformed, label\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "pC-D9a8xSZNd"
   },
   "outputs": [],
   "source": [
    "train_data = CatsDogsDataset(train_list, transform=train_transforms)\n",
    "valid_data = CatsDogsDataset(valid_list, transform=test_transforms)\n",
    "test_data = CatsDogsDataset(test_list, transform=test_transforms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "-mz9qUsLSZVU"
   },
   "outputs": [],
   "source": [
    "train_loader = DataLoader(dataset = train_data, batch_size=batch_size, shuffle=True )\n",
    "valid_loader = DataLoader(dataset = valid_data, batch_size=batch_size, shuffle=True)\n",
    "test_loader = DataLoader(dataset = test_data, batch_size=batch_size, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "JiIcaG1zSZLA",
    "outputId": "bd4d6e91-6c28-4b42-f242-246250dc55f2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20000 313\n"
     ]
    }
   ],
   "source": [
    "print(len(train_data), len(train_loader))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "hLLq3UCRSZIo",
    "outputId": "bc81dd28-197c-4c38-ecd1-7a8cc35d4703"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5000 79\n"
     ]
    }
   ],
   "source": [
    "print(len(valid_data), len(valid_loader))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TF9yMaRrSvmv"
   },
   "source": [
    "## Efficient Attention"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XED_6UFASx9r"
   },
   "source": [
    "### Linformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "EZ6KeBfgSnwg"
   },
   "outputs": [],
   "source": [
    "efficient_transformer = Linformer(\n",
    "    dim=128,\n",
    "    seq_len=49+1,  # 7x7 patches + 1 cls-token\n",
    "    depth=12,\n",
    "    heads=8,\n",
    "    k=64\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8VCgoZBvovkx"
   },
   "source": [
    "### Visual Transformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "rFmQvrLXSntZ"
   },
   "outputs": [],
   "source": [
    "model = ViT(\n",
    "    dim=128,\n",
    "    image_size=224,\n",
    "    patch_size=32,\n",
    "    num_classes=2,\n",
    "    transformer=efficient_transformer,\n",
    "    channels=3,\n",
    ").to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4u5YZG1eozIv"
   },
   "source": [
    "### Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "2Go1icggSnrB"
   },
   "outputs": [],
   "source": [
    "# loss function\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "# optimizer\n",
    "optimizer = optim.Adam(model.parameters(), lr=lr)\n",
    "# scheduler\n",
    "scheduler = StepLR(optimizer, step_size=1, gamma=gamma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
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    "colab": {
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      "Epoch : 18 - loss : 0.5921 - acc: 0.6767 - val_loss : 0.5889 - val_acc: 0.6816\n",
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      "Epoch : 19 - loss : 0.5896 - acc: 0.6822 - val_loss : 0.5917 - val_acc: 0.6699\n",
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      "Epoch : 20 - loss : 0.5887 - acc: 0.6786 - val_loss : 0.5836 - val_acc: 0.6855\n",
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   "source": [
    "for epoch in range(epochs):\n",
    "    epoch_loss = 0\n",
    "    epoch_accuracy = 0\n",
    "\n",
    "    for data, label in tqdm(train_loader):\n",
    "        data = data.to(device)\n",
    "        label = label.to(device)\n",
    "\n",
    "        output = model(data)\n",
    "        loss = criterion(output, label)\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        acc = (output.argmax(dim=1) == label).float().mean()\n",
    "        epoch_accuracy += acc / len(train_loader)\n",
    "        epoch_loss += loss / len(train_loader)\n",
    "\n",
    "    with torch.no_grad():\n",
    "        epoch_val_accuracy = 0\n",
    "        epoch_val_loss = 0\n",
    "        for data, label in valid_loader:\n",
    "            data = data.to(device)\n",
    "            label = label.to(device)\n",
    "\n",
    "            val_output = model(data)\n",
    "            val_loss = criterion(val_output, label)\n",
    "\n",
    "            acc = (val_output.argmax(dim=1) == label).float().mean()\n",
    "            epoch_val_accuracy += acc / len(valid_loader)\n",
    "            epoch_val_loss += val_loss / len(valid_loader)\n",
    "\n",
    "    print(\n",
    "        f\"Epoch : {epoch+1} - loss : {epoch_loss:.4f} - acc: {epoch_accuracy:.4f} - val_loss : {epoch_val_loss:.4f} - val_acc: {epoch_val_accuracy:.4f}\\n\"\n",
    "    )\n"
   ]
  },
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   "execution_count": null,
   "metadata": {
    "id": "kl7r45-FUmfC"
   },
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   "source": []
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