quickstart_colab.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "quickstart-colab.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMx+BaaVGeSat4b53xsfGc6",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/dondongwon/MLPDataset/blob/main/quickstart_colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This is a quickstart tutorial on colab. In order to quickly explore the dataset on colab, you only need to do the following few steps.\n",
        "\n",
        "1. Install Transformers (Hugging Face) (This is the **ONLY** package that needs to be installed on Colab)\n",
        "\n",
        "2. Mount and Upload dataset \n",
        "\n",
        "3. Instantiate dataloader class (LP Dataset)\n",
        "\n",
        "...and you're done! "
      ],
      "metadata": {
        "id": "eXQHEqxmcYsD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Install huggingface transformer library\n",
        "! pip install transformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gEudkaV7V2bt",
        "outputId": "63943ac9-0937-4379-f510-61805be1c77f"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting transformers\n",
            "  Downloading transformers-4.21.2-py3-none-any.whl (4.7 MB)\n",
            "\u001b[K     |████████████████████████████████| 4.7 MB 5.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.64.0)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2022.6.2)\n",
            "Collecting huggingface-hub<1.0,>=0.1.0\n",
            "  Downloading huggingface_hub-0.9.1-py3-none-any.whl (120 kB)\n",
            "\u001b[K     |████████████████████████████████| 120 kB 45.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.3)\n",
            "Collecting tokenizers!=0.11.3,<0.13,>=0.11.1\n",
            "  Downloading tokenizers-0.12.1-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (6.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 6.6 MB 41.7 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.21.6)\n",
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            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.12.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0,>=0.1.0->transformers) (4.1.1)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers) (3.0.9)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.8.1)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2022.6.15)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n",
            "Installing collected packages: tokenizers, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.9.1 tokenizers-0.12.1 transformers-4.21.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#upload dataset such that you can use the dataset on colab\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')\n",
        "\n",
        "#Make sure the dataset is uploaded to your google Drive, if you upload onto your main directory, \n",
        "#you can find it 'Drive/mlpdataset.zip', assuming you've named it mlpdataset.zip\n",
        "#Then unzip it such that google colab can access it \n",
        "!unzip gdrive/My\\ Drive/mlpdataset.zip\n",
        "#Some useful steps here: https://www.aboutdatablog.com/post/how-to-successfully-add-large-data-sets-to-google-drive-and-use-them-in-google-colab\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LPxWArtJWdcP",
        "outputId": "cefdfb48-6333-4388-f0d0-38f49e15a0f0"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "5SV-A4JvUO8j"
      },
      "outputs": [],
      "source": [
        "#Load the dataloader \n",
        "\n",
        "import os\n",
        "import sys\n",
        "import math\n",
        "import random\n",
        "import glob\n",
        "import pdb\n",
        "import scipy\n",
        "import numpy as np\n",
        "import json as json\n",
        "import torch\n",
        "import torch.utils.data as data\n",
        "import torchvision.transforms as transforms\n",
        "from transformers import AutoTokenizer, BertModel, AutoModelForQuestionAnswering, BertTokenizerFast, ViltProcessor, ViltModel\n",
        "\n",
        "from PIL import Image\n",
        "\n",
        "\n",
        "class LPDataset(data.Dataset):\n",
        "\n",
        "  def __init__(self, cap_json, fig_json, connect_json, rootdir, wemb_type, transform=None, ids=None):\n",
        "    \"\"\"\n",
        "    Args:\n",
        "      json: full_dataset.\n",
        "      transform: transformer for image.\n",
        "    \"\"\"\n",
        "\n",
        "    # if ids provided by get_paths, use split-specific ids\n",
        "    self.ids = [item for sublist in list(connect_json.values()) for item in sublist] # all ids\n",
        "    self.transform = transform\n",
        "    self.cap_json = cap_json\n",
        "    self.fig_json = fig_json\n",
        "    self.connect_json = connect_json\n",
        "    self.root = rootdir\n",
        "\n",
        "    self.wemb_type = wemb_type\n",
        "\n",
        "    if 'bert' in self.wemb_type:\n",
        "      self.bert_tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')\n",
        "\n",
        "    if 'vilt' in self.wemb_type:\n",
        "      self.vilt_processor = ViltProcessor.from_pretrained(\"dandelin/vilt-b32-mlm\")\n",
        "    \n",
        "  def __len__(self):\n",
        "    return len(self.ids)\n",
        "\n",
        "\n",
        "  def __getitem__(self, index):\n",
        "    sent, img_id, path, image, ocr, pointers = self.get_raw_item(index)\n",
        "    ocr_text = \" \".join([o['text'] for o in ocr])\n",
        "\n",
        "    if self.transform is not None:\n",
        "      image = self.transform(image)\n",
        "      \n",
        "    if 'bert' in self.wemb_type:\n",
        "      spoken_text = self.bert_tokenizer(sent,max_length=512, padding='max_length',truncation=True, return_tensors='pt')\n",
        "      spoken_target = spoken_text['input_ids'].squeeze()\n",
        "      ocr_target = self.bert_tokenizer(ocr_text,max_length=40, padding='max_length',truncation=True, add_special_tokens = False, return_tensors='pt')['input_ids']\n",
        "\n",
        "    if 'vilt' in self.wemb_type:\n",
        "      fig_ocr = self.vilt_processor(image, ocr_text, max_length=40, padding='max_length',truncation=True, return_tensors=\"pt\")\n",
        "      \n",
        "      pointer_target =  torch.zeros_like(spoken_target)\n",
        "      for point in pointers:\n",
        "        inds = torch.Tensor([i for i, x in enumerate(spoken_text.word_ids()) if x == point]).long()\n",
        "        pointer_target[inds] = 1 \n",
        "      return fig_ocr, spoken_target, pointer_target, index, img_id\n",
        "    \n",
        "    return  image, spoken_target, ocr_target, index, img_id\n",
        "\n",
        "  def get_raw_item(self, index):\n",
        "\n",
        "    img_id = self.ids[index]\n",
        "\n",
        "    words = [] \n",
        "    captions = self.fig_json[img_id]['captions']\n",
        "    for word in captions:\n",
        "      words.append(str(word['Word']))\n",
        "\n",
        "    sentence = \" \".join(words)\n",
        "\n",
        "\n",
        "    scene_id = self.fig_json[img_id]['scene_id']\n",
        "    \n",
        "    if scene_id[0] == os.path.basename(self.root):\n",
        "      _ = scene_id.pop(0)\n",
        "    \n",
        "    \n",
        "    path = os.path.join(self.root, \"/\".join(scene_id)+\".jpg\")\n",
        "    image = Image.open(path).convert('RGB')\n",
        "\n",
        "    #crop image here\n",
        "    cr = self.fig_json[img_id]['slide_figure']\n",
        "    x = cr[\"left\"]\n",
        "    y = cr[\"top\"]\n",
        "    w = cr[\"width\"]\n",
        "    h = cr[\"height\"]\n",
        "\n",
        "    # print(cr)\n",
        "    image = image.crop((x,y,x + w, y + h))\n",
        "    ocr =  self.fig_json[img_id]['slide_text']\n",
        "    pointers = self.fig_json[img_id]['pointers']\n",
        "    \n",
        "    return sentence, img_id, path, image, ocr, pointers\n",
        "\n",
        "\n",
        "def get_image_transform():\n",
        "  normalizer = transforms.Normalize(mean=[0.485, 0.456, 0.406],\n",
        "                                    std=[0.229, 0.224, 0.225])\n",
        "  t_list = []\n",
        "\n",
        "  t_list = [transforms.Resize(256), transforms.CenterCrop(224)]\n",
        "  t_end = [transforms.ToTensor(), normalizer]\n",
        "  transform = transforms.Compose(t_list + t_end)\n",
        "  return transform\n",
        "\n",
        "def get_image_transform_default():\n",
        "  t_list = []\n",
        "\n",
        "  t_list = [transforms.Resize(256)]\n",
        "  t_end = [transforms.ToTensor()]\n",
        "  transform = transforms.Compose(t_list + t_end)\n",
        "  return transform\n",
        "\n",
        "def default_collate_fn(data, caption_lim = 512):\n",
        "  # Sort a data list by sentence length\n",
        "    data.sort(key=lambda x: len(x[1]), reverse=True)\n",
        "    images, spoken_target, ocr_target, index, img_ids = zip(*data)\n",
        "    # Merge images (convert tuple of 3D tensor to 4D tensor)\n",
        "    images = torch.stack(images, 0)\n",
        "\n",
        "    cap_lengths = torch.tensor([len(cap) if len(cap) <= caption_lim else caption_lim for cap in spoken_target])\n",
        "    spoken_output = torch.zeros(len(spoken_target), caption_lim).long()\n",
        "    \n",
        "\n",
        "    for i, cap in enumerate(spoken_target):\n",
        "      end = cap_lengths[i]\n",
        "      if end <= caption_lim:\n",
        "        spoken_output[i, :end] = cap[:end]\n",
        "      else:\n",
        "        cap_lengths[i] = caption_lim\n",
        "        spoken_output[i, :end] = cap[:caption_lim]\n",
        "    return images, spoken_output, ocr_target, cap_lengths, index, img_ids\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sp = 'anat-1'\n",
        "\n",
        "#define where data is uploaded, by default, the following should work\n",
        "root_data_dir = 'data'\n",
        "sp_data_dir = '{}/{}'.format(root_data_dir, sp)\n",
        "\n",
        "#read jsons \n",
        "with open(\"{}/{}/{}_figs.json\".format(root_data_dir, sp,sp), 'r') as f:\n",
        " fig_json = json.loads(f.read())\n",
        "\n",
        "with open(\"{}/{}/{}.json\".format(root_data_dir, sp,sp), 'r') as j:\n",
        " cap_json = json.loads(j.read())\n",
        "\n",
        "with open(\"{}/{}/{}_capfig.json\".format(root_data_dir, sp,sp), 'r') as c:\n",
        " connect_json = json.loads(c.read())\n",
        "\n",
        "#load data\n",
        "transform = get_image_transform_default() #use get_image_transform() for normalization + cropping + resizing\n",
        "wemb_type = 'bert'\n",
        "dataset = LPDataset(cap_json, fig_json, connect_json, sp_data_dir, wemb_type, transform)\n",
        "loader = torch.utils.data.DataLoader(dataset=dataset,\n",
        "                                        batch_size=1,\n",
        "                                        shuffle=True,\n",
        "                                        pin_memory=True,\n",
        "                                        num_workers=1,\n",
        "                                        collate_fn = default_collate_fn\n",
        "                                        )"
      ],
      "metadata": {
        "id": "GK6Z_8ScVaRP"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "## functions to show an image\n",
        "def imshow(img):\n",
        "    #img = img / 2 + 0.5     # unnormalize\n",
        "    npimg = img.numpy()\n",
        "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
        "\n",
        "## get some random training images\n",
        "\n",
        "'''\n",
        "Each sample constitutes of \n",
        "\n",
        "images: normalized images of figures (3 x 224 x 224)\n",
        "spoken_output: word tokens for spoken language (glove or bert)\n",
        "ocr_target: word tokens for ocr on-slide text (glove or bert)\n",
        "cap_lengths: length of captions\n",
        "index: number image ids\n",
        "img_ids: string image ids\n",
        "'''\n",
        "\n",
        "dataiter = iter(loader)\n",
        "images, spoken_output, ocr_target, cap_lengths, index, img_ids  = dataiter.next()"
      ],
      "metadata": {
        "id": "n2TKjI6VZP6v"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#images (transform not applied)\n",
        "\n",
        "plt.imshow(images[0].permute(1,2,0).numpy(), interpolation='nearest')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "wUjyO83qa7fP",
        "outputId": "10ddf44a-f155-4e61-80e5-9a69b251433b"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#example of spoken_output \n",
        "\n",
        "bert_tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')\n",
        "bert_tokenizer.decode(spoken_output[0], skip_special_tokens = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 103
        },
        "id": "yeEsEtO6bD9P",
        "outputId": "d7c684ac-21cb-4109-a15c-a50821d40ab0"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'so this illustration just goes to show you that many different types of stimuli are able to stimulate the respiratory centers in the medulla and the pons so it could be peripheral chemoreceptors it could be central chemoreceptors and receptors and muscles and joints irritants that we spoke about earlier stress stretch receptors in the lungs are again it could be coming from higher brain centers pencil and in your thoughts that you may be experienced for stimulus t - pain all these will end up triggering the respiratory centers and then your breathing gets affected'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#example of OCR\n",
        "\n",
        "bert_tokenizer.decode(ocr_target[0][0], skip_special_tokens = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "0EUEQ5aNcLeT",
        "outputId": "1913cc0d-32cb-403a-e021-70c99f25e5e3"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'figure higher brain centers control over other receptors and emotional stimuli acting through the hypothalamus respiratory centers and peripheral chemoreceptors stretch receptors in lungs central chemoreceptors irritan'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ymM1Kf4ecQEZ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}