image_captioning_dnd_v2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "V100"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "pip install kaggle"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zh9U5WSv-6eU",
        "outputId": "6fdd91cd-f14c-4bb6-a52d-be31780081a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (1.5.16)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle) (1.16.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from kaggle) (2023.11.17)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.31.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from kaggle) (4.66.1)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.10/dist-packages (from kaggle) (8.0.1)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.0.7)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from kaggle) (6.1.0)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->kaggle) (0.5.1)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.10/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.6)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "\n",
        "files.upload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "id": "4oCsYAsv-_3x",
        "outputId": "a4126922-4865-4f64-e72c-9ec30448d242"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-9586784f-dd3f-4fed-ae71-9b58c7bceb8b\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-9586784f-dd3f-4fed-ae71-9b58c7bceb8b\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"michaelwalton\",\"key\":\"d0c6d0a438ac4b62a6629e95e90b8fe9\"}'}"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NS0SHAnjhKHI",
        "outputId": "b9374885-653d-495d-e475-747c54d94a26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "!chmod 600 ~/.kaggle/kaggle.json\n",
        "\n",
        "!kaggle datasets download -d sayanf/flickr8k"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F955CogA_I9V",
        "outputId": "7502a3fb-1070-4d46-a5e0-c189a8e9fb56"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading flickr8k.zip to /content\n",
            "100% 1.04G/1.04G [00:27<00:00, 43.0MB/s]\n",
            "100% 1.04G/1.04G [00:27<00:00, 40.0MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip flickr8k.zip\n",
        "!mv Flickr8k_Dataset Flickr8k_images\n",
        "!mv Flickr8k_text Flickr8k_annotations\n",
        "!ls"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1YLHACSu_LIo",
        "outputId": "2ec5c5ca-af89-490a-c13a-33b89fc40c0d"
      },
      "execution_count": null,
      "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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            "  inflating: Flickr8k_text/CrowdFlowerAnnotations.txt  \n",
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            "  inflating: Flickr8k_text/Flickr8k.lemma.token.txt  \n",
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            "  inflating: Flickr8k_text/readme.txt  \n",
            "drive  Flickr8k_annotations  Flickr8k_images  flickr8k.zip  kaggle.json  sample_data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "piAYn9Aw8c8E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "j8aICLm894Oq"
      },
      "outputs": [],
      "source": [
        "# linear algebra\n",
        "import numpy as np\n",
        "# data processing, CSV file I / O (e.g. pd.read_csv)\n",
        "import pandas as pd\n",
        "import os\n",
        "import tensorflow as tf\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.models import Model\n",
        "from keras.layers import Flatten, Dense, LSTM, Dropout, Embedding, Activation\n",
        "from keras.layers import concatenate, BatchNormalization, Input\n",
        "from keras.utils import to_categorical, plot_model\n",
        "from keras.applications.inception_v3 import InceptionV3, preprocess_input\n",
        "import matplotlib.pyplot as plt  # for plotting data\n",
        "import cv2\n",
        "\n",
        "import nltk\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "from collections import Counter"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def load_description(text):\n",
        "    mapping = dict()\n",
        "    for line in text.split(\"\\n\"):\n",
        "        token = line.split(\"\\t\")\n",
        "        if len(line) < 2:   # remove short descriptions\n",
        "            continue\n",
        "        img_id = token[0].split('.')[0] # name of the image\n",
        "        img_des = token[1]              # description of the image\n",
        "        if img_id not in mapping:\n",
        "            mapping[img_id] = list()\n",
        "        mapping[img_id].append(img_des)\n",
        "    return mapping\n",
        "\n",
        "token_path = '/content/Flickr8k_annotations/Flickr8k.token.txt'\n",
        "text = open(token_path, 'r', encoding = 'utf-8').read()\n",
        "descriptions = load_description(text)\n",
        "print(descriptions['1000268201_693b08cb0e'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i8JRy3Bd-wzA",
        "outputId": "85be203a-9c34-4fd6-e7f4-d04b0234961b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['A child in a pink dress is climbing up a set of stairs in an entry way .', 'A girl going into a wooden building .', 'A little girl climbing into a wooden playhouse .', 'A little girl climbing the stairs to her playhouse .', 'A little girl in a pink dress going into a wooden cabin .']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Download the WordNet resource for lemmatization\n",
        "nltk.download('wordnet')\n",
        "nltk.download('punkt')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TQqZH4ipIJE8",
        "outputId": "137b5723-a395-4561-8db0-654543f3250c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def to_vocab(desc):\n",
        "    words = set()\n",
        "    for key in desc.keys():\n",
        "        for line in desc[key]:\n",
        "            words.update(line.split())\n",
        "    return words\n",
        "\n",
        "vocab = to_vocab(descriptions)"
      ],
      "metadata": {
        "id": "HCNQRsN0JMtO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import glob\n",
        "\n",
        "default_image_path = '/content/Flickr8k_images/'\n",
        "\n",
        "def Load_Data(path):\n",
        "    images = path #'/content/Flickr8k_images/'\n",
        "\n",
        "    def load_image_paths(split):\n",
        "        image_paths = []\n",
        "        with open(f'/content/Flickr8k_annotations/Flickr_8k.{split}Images.txt', 'r') as f:\n",
        "            lines = f.readlines()\n",
        "            for l in lines:\n",
        "                image_paths.append(f'{images}{l.strip()}')\n",
        "        return image_paths\n",
        "\n",
        "    image_paths = load_image_paths('train')\n",
        "    # Create a list of all image names in the directory\n",
        "    #img = glob.glob(images_paths + '*.jpg')\n",
        "\n",
        "    train_path = '/content/Flickr8k_annotations/Flickr_8k.trainImages.txt'\n",
        "    train_images = open(train_path, 'r', encoding = 'utf-8').read().split(\"\\n\")\n",
        "    train_img = []  # list of all images in training set\n",
        "    for im in image_paths:\n",
        "        train_img.append(im)\n",
        "\n",
        "    # load descriptions of training set in a dictionary. Name of the image will act as ey\n",
        "    def load_clean_descriptions(des, dataset):\n",
        "        dataset_des = dict()\n",
        "        for key, des_list in des.items():\n",
        "            if key+'.jpg' in dataset:\n",
        "                if key not in dataset_des:\n",
        "                    dataset_des[key] = list()\n",
        "                for line in des_list:\n",
        "                    desc = '<SOS> ' + line + ' <EOS>'\n",
        "                    dataset_des[key].append(desc)\n",
        "        return dataset_des\n",
        "\n",
        "    train_descriptions = load_clean_descriptions(descriptions, train_images)\n",
        "    print(train_descriptions['1000268201_693b08cb0e'])\n",
        "    return train_descriptions, train_img"
      ],
      "metadata": {
        "id": "JBZP9Q5sQFYg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_descriptions, train_img = Load_Data(default_image_path)\n",
        "len(train_img)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oPRDOKCJUyYi",
        "outputId": "4bc2f0ee-28d7-4c33-d773-9072d8b9eb50"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['<SOS> A child in a pink dress is climbing up a set of stairs in an entry way . <EOS>', '<SOS> A girl going into a wooden building . <EOS>', '<SOS> A little girl climbing into a wooden playhouse . <EOS>', '<SOS> A little girl climbing the stairs to her playhouse . <EOS>', '<SOS> A little girl in a pink dress going into a wooden cabin . <EOS>']\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6000"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class Vocabulary:\n",
        "    def __init__(self):\n",
        "        self.word2idx = {}\n",
        "        self.idx2word = {}\n",
        "        self.idx = 0\n",
        "\n",
        "    def add_word(self, word):\n",
        "        if word not in self.word2idx:\n",
        "            self.word2idx[word] = self.idx\n",
        "            self.idx2word[self.idx] = word\n",
        "            self.idx += 1\n",
        "\n",
        "    def build_vocabulary(self, captions_list):\n",
        "        self.add_word('<SOS>')\n",
        "        self.add_word('<EOS>')\n",
        "        for captions in captions_list:\n",
        "            for caption in captions:\n",
        "                words = nltk.word_tokenize(caption.lower())\n",
        "                for word in words:\n",
        "                    self.add_word(WordNetLemmatizer().lemmatize(word))"
      ],
      "metadata": {
        "id": "-p3-kayMVG4T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vocab = Vocabulary()\n",
        "\n",
        "# Build the vocabulary from the captions in the training dataset\n",
        "vocab.build_vocabulary(train_descriptions.values())\n",
        "\n",
        "\n",
        "VOCAB_SIZE = len(vocab.word2idx)\n",
        "\n",
        "# Print the size of the vocabulary\n",
        "print(f\"Vocabulary size: {VOCAB_SIZE}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FoY7ZYPwVKIT",
        "outputId": "26a74d97-8678-44cc-9c69-b41f1779be0e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Vocabulary size: 6659\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Set up GPU or CPU\n",
        "\n",
        "device_name = tf.test.gpu_device_name()\n",
        "if device_name != '/device:GPU:0':\n",
        "  raise SystemError('GPU device not found')\n",
        "print('Found GPU at: {}'.format(device_name))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L-U6ljRo_iGt",
        "outputId": "8aa0a910-4f73-411f-dcba-e8aa4721dde0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found GPU at: /device:GPU:0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "device_name = tf.test.gpu_device_name()"
      ],
      "metadata": {
        "id": "-UF_GlJf__wK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.preprocessing.image import load_img, img_to_array\n",
        "\n",
        "def EncoderCNN(data, folder_path):\n",
        "    def preprocess_img(img_path):\n",
        "        # inception v3 excepts img in 299 * 299 * 3\n",
        "        img = load_img(img_path, target_size = (299, 299))\n",
        "        x = img_to_array(img)\n",
        "        # Add one more dimension\n",
        "        x = np.expand_dims(x, axis = 0)\n",
        "        x = preprocess_input(x)\n",
        "        return x\n",
        "\n",
        "    def encode(image):\n",
        "        image = preprocess_img(image)\n",
        "        vec = model.predict(image)\n",
        "        vec = np.reshape(vec, (vec.shape[1]))\n",
        "        return vec\n",
        "\n",
        "    with tf.device('/device:GPU:0'):\n",
        "        base_model = InceptionV3(weights = 'imagenet')\n",
        "        model = Model(base_model.input, base_model.layers[-2].output)\n",
        "    # run the encode function on all train images and store the feature vectors in a list\n",
        "    encoding_train = {}\n",
        "    print(len(data))\n",
        "    for i, img in enumerate(data):\n",
        "        encoding_train[img[len(folder_path):]] = encode(img)\n",
        "        if i == 200:\n",
        "            break\n",
        "\n",
        "    return encoding_train"
      ],
      "metadata": {
        "id": "5dl7t0mFkhyi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "encoding_train = EncoderCNN(train_img, default_image_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_ivPcx9diyss",
        "outputId": "187fab22-94c9-4665-a82c-17edc6a8c2a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max_length = 50\n",
        "\n",
        "def Embed_Matrix(encodings, descriptions):\n",
        "    X1, X2, y = list(), list(), list()\n",
        "    #for key, des_list in train_descriptions.items():\n",
        "        #pic = encoding_train[key + '.jpg']\n",
        "    for key, encoding in encodings.items():\n",
        "        pic = encoding\n",
        "        des_list = descriptions[key[:-4]]\n",
        "        for cap in des_list:\n",
        "            seq = [vocab.word2idx[word] for word in cap.split(' ') if word in vocab.word2idx]\n",
        "            for i in range(1, len(seq)):\n",
        "                in_seq, out_seq = seq[:i], seq[i]\n",
        "                in_seq = pad_sequences([in_seq], maxlen = max_length)[0]\n",
        "                out_seq = to_categorical([out_seq], num_classes = VOCAB_SIZE)[0]\n",
        "                # store\n",
        "                X1.append(pic)\n",
        "                X2.append(in_seq)\n",
        "                y.append(out_seq)\n",
        "\n",
        "    X2 = np.array(X2)\n",
        "    X1 = np.array(X1)\n",
        "    y = np.array(y)\n",
        "\n",
        "    # load glove vectors for embedding layer\n",
        "    embeddings_index = {}\n",
        "    golve_path ='/content/drive/MyDrive/Masters/ELTE/DND/Image Captioning/glove.6B.200d.txt'\n",
        "    glove = open(golve_path, 'r', encoding = 'utf-8').read()\n",
        "    for line in glove.split(\"\\n\"):\n",
        "        values = line.split(\" \")\n",
        "        word = values[0]\n",
        "        indices = np.asarray(values[1: ], dtype = 'float32')\n",
        "        embeddings_index[word] = indices\n",
        "\n",
        "    emb_dim = 200\n",
        "    emb_matrix = np.zeros((VOCAB_SIZE, emb_dim))\n",
        "    for word, i in vocab.word2idx.items():\n",
        "        emb_vec = embeddings_index.get(word)\n",
        "        if emb_vec is not None:\n",
        "            emb_matrix[i] = emb_vec\n",
        "    emb_matrix.shape\n",
        "    return emb_matrix, emb_dim, X1, X2, y"
      ],
      "metadata": {
        "id": "UrJyfQRaW7Yw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# define the model\n",
        "def DecoderRNN(embed_dim):\n",
        "    ip1 = Input(shape = (2048, ))\n",
        "    fe1 = Dropout(0.2)(ip1)\n",
        "    fe2 = Dense(256, activation = 'relu')(fe1)\n",
        "    ip2 = Input(shape = (max_length, ))\n",
        "    se1 = Embedding(VOCAB_SIZE, embed_dim, mask_zero = True)(ip2)\n",
        "    se2 = Dropout(0.2)(se1)\n",
        "    se3 = LSTM(256)(se2)\n",
        "    decoder1 = concatenate([fe2, se3])\n",
        "    decoder2 = Dense(256, activation = 'relu')(decoder1)\n",
        "    outputs = Dense(VOCAB_SIZE, activation = 'softmax')(decoder2)\n",
        "    model = Model(inputs = [ip1, ip2], outputs = outputs)\n",
        "    return model"
      ],
      "metadata": {
        "id": "FTmQPH_BqDR-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def Train(model, embed_matrix, X1, X2, y, epochs, batch_size):\n",
        "    with tf.device('/device:GPU:0'):\n",
        "        model.layers[2].set_weights([embed_matrix])\n",
        "        model.layers[2].trainable = False\n",
        "        model.compile(loss = 'categorical_crossentropy', optimizer = 'adam')\n",
        "        model.fit([X1, X2], y, epochs = epochs, batch_size = batch_size)\n",
        "        return model\n",
        "# you can increase the number of epochs for better results"
      ],
      "metadata": {
        "id": "x6nPt5xRqlW4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def greedy_search(pic, model):\n",
        "    start = '<SOS>'\n",
        "    for i in range(max_length):\n",
        "        seq = [vocab.word2idx[word] for word in start.split() if word in vocab.word2idx]\n",
        "        seq = pad_sequences([seq], maxlen = max_length)\n",
        "        print(seq.shape)\n",
        "        print(pic.shape)\n",
        "        yhat = model.predict([pic, seq])\n",
        "        yhat = np.argmax(yhat)\n",
        "        word = vocab.idx2word[yhat]\n",
        "        start += ' ' + word\n",
        "        if word == '<EOS>':\n",
        "            break\n",
        "    final = start.split()\n",
        "    final = final[1:-1]\n",
        "    final = ' '.join(final)\n",
        "    return final"
      ],
      "metadata": {
        "id": "AxTTVFlWqzEP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Test Data"
      ],
      "metadata": {
        "id": "vpc4P3iXobLg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "imgs = glob.glob('/content/drive/MyDrive/Masters/ELTE/DND/Image Captioning/Assignment 3 Test set/' + '*.jpg')"
      ],
      "metadata": {
        "id": "FBR3xGQdlesc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "encodings = EncoderCNN(imgs, '/content/drive/MyDrive/Masters/ELTE/DND/Image Captioning/Assignment 3 Test set/')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mZqhRDnqnn5i",
        "outputId": "a76273e3-7f76-466d-f932-0f5924ba5acf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "embed_matrix, embed_dim, X1, X2, y = Embed_Matrix(encoding_train, descriptions)"
      ],
      "metadata": {
        "id": "Z7DNWW1fpD9R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = DecoderRNN(embed_dim)"
      ],
      "metadata": {
        "id": "xfXxpLReo6KF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Train(model, embed_matrix, X1, X2, y, 5, 16)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yPfk7ieEzKFl",
        "outputId": "36f22860-c6ae-4a5e-e3c6-24d8cee4b758"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "1400/1400 [==============================] - 189s 131ms/step - loss: 3.9942\n",
            "Epoch 2/5\n",
            "1400/1400 [==============================] - 186s 133ms/step - loss: 3.5518\n",
            "Epoch 3/5\n",
            "1400/1400 [==============================] - 184s 131ms/step - loss: 3.2456\n",
            "Epoch 4/5\n",
            "1400/1400 [==============================] - 187s 134ms/step - loss: 3.0050\n",
            "Epoch 5/5\n",
            "1400/1400 [==============================] - 186s 133ms/step - loss: 2.7855\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = {}\n",
        "for key, img in encodings.items():\n",
        "    x = img.reshape(img.shape[0],1)\n",
        "    data[key] = greedy_search(x.T, model)"
      ],
      "metadata": {
        "id": "SHKjUp2Zol6l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(list(data.items()), columns=['image_id', 'predicted_caption'])\n",
        "df"
      ],
      "metadata": {
        "id": "LeDXuIu1uSt3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_csv('test_captions.csv')\n",
        "!cp test_captions.csv \"/content/drive/MyDrive/Masters/ELTE/DND/Image Captioning\""
      ],
      "metadata": {
        "id": "9ojEXDIMvTio"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# importing Image class from PIL package\n",
        "from PIL import Image\n",
        "\n",
        "for dp in data\n",
        "# creating a object\n",
        "im = Image.open(r\"C:\\Users\\System-Pc\\Desktop\\home.png\")\n",
        "\n",
        "im.show()"
      ],
      "metadata": {
        "id": "kL-YL52U64WV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "MUoTQwp_KaGW"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}