vaq.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "vaq.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/beingraja/visual_question_answering/blob/main/vaq.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4LYDB6O0nlXV",
        "outputId": "aef2801e-7759-4876-a716-a15a8ece3557"
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sat Jul  3 04:35:02 2021       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 465.27       Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   37C    P8     9W /  70W |      0MiB / 15109MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2wVLfdM5xuV1",
        "outputId": "78bd68f6-ed2d-46a3-b3e9-420b28566937"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lUhGuX_2pixz",
        "outputId": "23322a72-3013-4f2b-c52a-66b27c1a09ed"
      },
      "source": [
        "!wget http://images.cocodataset.org/zips/val2014.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-07-03 04:36:12--  http://images.cocodataset.org/zips/val2014.zip\n",
            "Resolving images.cocodataset.org (images.cocodataset.org)... 52.216.110.195\n",
            "Connecting to images.cocodataset.org (images.cocodataset.org)|52.216.110.195|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 6645013297 (6.2G) [application/zip]\n",
            "Saving to: ‘val2014.zip’\n",
            "\n",
            "val2014.zip         100%[===================>]   6.19G  42.5MB/s    in 2m 32s  \n",
            "\n",
            "2021-07-03 04:38:45 (41.7 MB/s) - ‘val2014.zip’ saved [6645013297/6645013297]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O84EVSZtpi02"
      },
      "source": [
        "!unzip /content/val2014.zip -d /content/images"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zpNl7aBVpi3s",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c431c4f8-961c-44b9-f8fe-8259977e8867"
      },
      "source": [
        "!wget https://s3.amazonaws.com/cvmlp/vqa/mscoco/vqa/v2_Questions_Val_mscoco.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-07-03 04:41:52--  https://s3.amazonaws.com/cvmlp/vqa/mscoco/vqa/v2_Questions_Val_mscoco.zip\n",
            "Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.217.161.128\n",
            "Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.217.161.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 3494929 (3.3M) [application/zip]\n",
            "Saving to: ‘v2_Questions_Val_mscoco.zip’\n",
            "\n",
            "v2_Questions_Val_ms 100%[===================>]   3.33M  --.-KB/s    in 0.07s   \n",
            "\n",
            "2021-07-03 04:41:53 (50.4 MB/s) - ‘v2_Questions_Val_mscoco.zip’ saved [3494929/3494929]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YiJrKNCTpi6Z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cddb3545-7812-4767-d2c6-9285ee78f32b"
      },
      "source": [
        "!unzip /content/v2_Questions_Val_mscoco.zip -d /content/question"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  /content/v2_Questions_Val_mscoco.zip\n",
            "  inflating: /content/question/v2_OpenEnded_mscoco_val2014_questions.json  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vvZ-ybCsttTE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c3ed3f00-2231-41a3-d8cd-2f8dab311cf5"
      },
      "source": [
        "!wget https://s3.amazonaws.com/cvmlp/vqa/mscoco/vqa/v2_Annotations_Val_mscoco.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-07-03 04:41:53--  https://s3.amazonaws.com/cvmlp/vqa/mscoco/vqa/v2_Annotations_Val_mscoco.zip\n",
            "Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.217.161.128\n",
            "Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.217.161.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 10518930 (10M) [application/zip]\n",
            "Saving to: ‘v2_Annotations_Val_mscoco.zip’\n",
            "\n",
            "v2_Annotations_Val_ 100%[===================>]  10.03M  --.-KB/s    in 0.1s    \n",
            "\n",
            "2021-07-03 04:41:53 (70.5 MB/s) - ‘v2_Annotations_Val_mscoco.zip’ saved [10518930/10518930]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dihzA92-tr-J",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7d448247-7c77-4929-c2ff-ba043425fc8c"
      },
      "source": [
        "!unzip /content/v2_Annotations_Val_mscoco.zip -d nnotions"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  /content/v2_Annotations_Val_mscoco.zip\n",
            "  inflating: nnotions/v2_mscoco_val2014_annotations.json  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z3mZsYOdtsBG"
      },
      "source": [
        "import  json\n",
        "d  = json.load(open('/content/nnotions/v2_mscoco_val2014_annotations.json'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ypNNZpemW5Bb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ebd31d42-bcd5-410c-b9dd-deb1a37904f6"
      },
      "source": [
        "import  json\n",
        "ques  = json.load(open('/content/question/v2_OpenEnded_mscoco_val2014_questions.json'))\n",
        "ques['questions'][0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'image_id': 262148,\n",
              " 'question': 'Where is he looking?',\n",
              " 'question_id': 262148000}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JQoFAgFruDv5",
        "outputId": "beeae104-3ba5-4b54-ff17-dd2fac311d8f"
      },
      "source": [
        "from tqdm import tqdm\n",
        "import cv2\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def img(id,size):\n",
        "  img = cv2.imread('/content/images/val2014/'+'COCO_val2014_000000'+str(id)+'.jpg')\n",
        "  img = cv2.resize(img,(size,size))\n",
        "  img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)\n",
        "  return img\n",
        "\n",
        "IMG_SIZE = 128\n",
        "DATASET_SIZE = 15000\n",
        "images = []\n",
        "questions = []\n",
        "answers = []\n",
        "\n",
        "for i in tqdm(range(35000,35000+DATASET_SIZE)):\n",
        "  img_id = d['annotations'][i]['image_id']\n",
        "  if len(str(img_id)) != 6:\n",
        "    zero = '0'*(6-len(str(img_id))) \n",
        "    img_id = zero + str(img_id)\n",
        "  image = img(img_id,IMG_SIZE)\n",
        "  images.append(image)\n",
        "\n",
        "  question = ques['questions'][i]['question']\n",
        "  questions.append(question)\n",
        "\n",
        "  answer = d['annotations'][i]['multiple_choice_answer']\n",
        "  answers.append(answer)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 15000/15000 [01:36<00:00, 155.98it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UwU2DfquL5LQ"
      },
      "source": [
        "answer_set = []\n",
        "for i in range(len(d['annotations'])):\n",
        "  answer_set.append(d['annotations'][i]['multiple_choice_answer'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "CzrxrkgNbBTt",
        "outputId": "f856467e-cf52-4f49-c55c-a75ef2f6652f"
      },
      "source": [
        "def eval(idx):\n",
        "  img  = images[idx]\n",
        "  norm = cv2.normalize(img, None, alpha=0, beta=1, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_32F)\n",
        "  que = questions[idx]\n",
        "  ans = answers[idx]\n",
        "  plt.imshow(norm)\n",
        "  print('QUESTION : ',que)\n",
        "  print('ANSWERS : ',ans)\n",
        "eval(20)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "QUESTION :  What color is the building?\n",
            "ANSWERS :  brown\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SaUpnZsqnndd"
      },
      "source": [
        "!pip install tensorflow\n",
        "!pip install tensorflow_hub\n",
        "!pip install bert-for-tf2\n",
        "!pip install sentencepiece"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ucYVD12snrrc",
        "outputId": "092f783a-9a2f-4358-e159-8185920fbc17"
      },
      "source": [
        "import tensorflow as tf\n",
        "import tensorflow_hub as hub\n",
        "\n",
        "print(\"TF version: \", tf.__version__)\n",
        "print(\"Hub version: \", hub.__version__)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TF version:  2.5.0\n",
            "Hub version:  0.12.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_s31GK22z9eb"
      },
      "source": [
        "from bert import bert_tokenization\n",
        "bert_layer = hub.KerasLayer(\"https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/1\",\n",
        " trainable=True)\n",
        "BertTokenizer = bert_tokenization.FullTokenizer\n",
        "vocab_file = bert_layer.resolved_object.vocab_file.asset_path.numpy()\n",
        "do_lower_case = bert_layer.resolved_object.do_lower_case.numpy()\n",
        "tokenizer =  bert_tokenization.FullTokenizer(vocab_file, do_lower_case)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MX8v4UX6z3m7",
        "outputId": "b599cf70-e7d8-4c71-dbef-d4a8687314a5"
      },
      "source": [
        "#preprocess\n",
        "import numpy as np\n",
        "for i in tqdm(range(len(questions))):\n",
        "  images[i] = (np.asarray(images[i]).astype('float32') - 127.5) / 127.5\n",
        "  answers[i] = answers[i].lower()\n",
        "  questions[i] = [\"[CLS]\"] + tokenizer.tokenize(questions[i]) + [\"[SEP]\"]\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 15000/15000 [00:03<00:00, 3913.98it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "_dz1aJ140b0c",
        "outputId": "0caa0306-1df2-4c6b-eb93-d95a0272d617"
      },
      "source": [
        "print(questions[10])\n",
        "print(answers[10])\n",
        "\n",
        "plt.imshow(images[10])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "['[CLS]', 'what', 'design', 'is', 'the', 'bag', '?', '[SEP]']\n",
            "flower\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7faa57902dd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N5q6YB-7z7tE",
        "outputId": "b48d5ee1-63e3-4e9f-d94f-e00feb12bea1"
      },
      "source": [
        "ans_dict = {}\n",
        "ans_dict_inv = {}\n",
        "y = []\n",
        "a = 0\n",
        "for i in answer_set:\n",
        "  if i not in ans_dict:\n",
        "    ans_dict[i] = a\n",
        "    ans_dict_inv[a] = i\n",
        "    a+= 1\n",
        "\n",
        "    \n",
        "for i in answers:\n",
        "   y.append(ans_dict[i])\n",
        "len(y),len(ans_dict),len(ans_dict_inv)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15000, 14008, 14008)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p89FZIb1r65r",
        "outputId": "b1e6f242-5c19-48e7-a0bc-df66d696ba05"
      },
      "source": [
        "import pickle\n",
        "\n",
        "\n",
        "\n",
        "# Store data (serialize)\n",
        "with open('ans_dict_inv.pickle', 'wb') as handle:\n",
        "    pickle.dump(ans_dict_inv, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
        "\n",
        "# Load data (deserialize)\n",
        "with open('ans_dict_inv.pickle', 'rb') as handle:\n",
        "    unserialized_data = pickle.load(handle)\n",
        "\n",
        "print(ans_dict_inv == unserialized_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "True\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v4UjCMguoL6s"
      },
      "source": [
        "def get_masks(tokens, max_seq_length):\n",
        "    \"\"\"Mask for padding\"\"\"\n",
        "    if len(tokens)>max_seq_length:\n",
        "        raise IndexError(\"Token length more than max seq length!\")\n",
        "    return [1]*len(tokens) + [0] * (max_seq_length - len(tokens))\n",
        "\n",
        "\n",
        "def get_segments(tokens, max_seq_length):\n",
        "    \"\"\"Segments: 0 for the first sequence, 1 for the second\"\"\"\n",
        "    if len(tokens)>max_seq_length:\n",
        "        raise IndexError(\"Token length more than max seq length!\")\n",
        "    segments = []\n",
        "    current_segment_id = 0\n",
        "    for token in tokens:\n",
        "        segments.append(current_segment_id)\n",
        "        if token == \"[SEP]\":\n",
        "            current_segment_id = 1\n",
        "    return segments + [0] * (max_seq_length - len(tokens))\n",
        "\n",
        "\n",
        "def get_ids(tokens, tokenizer, max_seq_length):\n",
        "    \"\"\"Token ids from Tokenizer vocab\"\"\"\n",
        "    token_ids = tokenizer.convert_tokens_to_ids(tokens)\n",
        "    input_ids = token_ids + [0] * (max_seq_length-len(token_ids))\n",
        "    return input_ids"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8TlKlhLM1ROz",
        "outputId": "41a2cefa-892f-4251-d5de-02cd91f94a1c"
      },
      "source": [
        "input_ids = []\n",
        "input_masks =  []                 \n",
        "input_segments = []\n",
        "max_seq_length = 128\n",
        "for i in tqdm(questions):    \n",
        "  input_id = get_ids(i, tokenizer, max_seq_length)\n",
        "  input_mask = get_masks(i, max_seq_length)\n",
        "  input_segment = get_segments(i, max_seq_length)\n",
        "  input_ids.append(input_id)\n",
        "  input_masks.append(input_mask)\n",
        "  input_segments.append(input_segment)\n",
        "\n",
        "print(len(input_ids))\n",
        "print(len(input_masks))\n",
        "print(len(input_segments))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 15000/15000 [00:00<00:00, 79684.98it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "15000\n",
            "15000\n",
            "15000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hbozmfIPo47P"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "input_ids = np.array(input_ids)\n",
        "input_masks = np.array(input_masks)\n",
        "input_segments = np.array(input_segments)\n",
        "y = np.array(y)\n",
        "images = np.array(images)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oyyIW9Ey-J2v"
      },
      "source": [
        "y = tf.keras.utils.to_categorical(\n",
        "    y, num_classes=len(ans_dict), dtype='float32'\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GZqLb8H-7KKc",
        "outputId": "91587e5b-798f-43a9-a519-326e860d35f0"
      },
      "source": [
        "input_ids.shape,images.shape,y.shape,input_masks.shape,input_segments.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((15000, 128),\n",
              " (15000, 128, 128, 3),\n",
              " (15000, 14008),\n",
              " (15000, 128),\n",
              " (15000, 128))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ENywPY2h3sWz"
      },
      "source": [
        "rn152 = tf.keras.applications.ResNet152V2(\n",
        "    include_top=False, weights='imagenet', input_tensor=None, input_shape=(128, 128, 3)\n",
        ")\n",
        "new_input = rn152.input\n",
        "hidden_layer = rn152.layers[-1].output\n",
        "flat = tf.keras.layers.Flatten()\n",
        "f = flat(hidden_layer)\n",
        "dense = tf.keras.layers.Dense(1024,activation='relu')(f)\n",
        "\n",
        "max_seq_length = 128 \n",
        "input_word_ids = tf.keras.layers.Input(shape=(max_seq_length,), dtype=tf.int32,\n",
        " name=\"input_word_ids\")\n",
        "input_mask = tf.keras.layers.Input(shape=(max_seq_length,), dtype=tf.int32,\n",
        " name=\"input_mask\")\n",
        "segment_ids = tf.keras.layers.Input(shape=(max_seq_length,), dtype=tf.int32,\n",
        " name=\"segment_ids\")\n",
        "pooled_output, sequence_output = bert_layer([input_word_ids, input_mask, segment_ids])\n",
        "f = flat(pooled_output)\n",
        "dense_bert = tf.keras.layers.Dense(1024,activation='relu')(pooled_output)\n",
        "\n",
        "con = tf.keras.layers.Concatenate()([dense,dense_bert])\n",
        "final_dense = tf.keras.layers.Dense(1024,activation='relu',)(con)\n",
        "final_dense = tf.keras.layers.Dropout(0.4)(final_dense)\n",
        "output = final_dense = tf.keras.layers.Dense(len(ans_dict),activation='softmax',)(final_dense)\n",
        "\n",
        "model = tf.keras.Model(inputs=[new_input,input_word_ids, input_mask, segment_ids],outputs= output) \n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0jmULkYeg81M"
      },
      "source": [
        "from tensorflow.keras.models import load_model\n",
        "model =  load_model('/content/drive/MyDrive/Colab Notebooks/vqa samples/vaq_model/model.h5',custom_objects={'KerasLayer':hub.KerasLayer})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F6ReXmsr3thi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8dcbc453-b892-4849-ccdb-0142859b5e0b"
      },
      "source": [
        "model.compile(tf.keras.optimizers.Adam(lr=1e-5), loss='categorical_crossentropy', metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:375: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  \"The `lr` argument is deprecated, use `learning_rate` instead.\")\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SiOkhg-sSfm9"
      },
      "source": [
        "def prediction(question,image):\n",
        "    image = (np.asarray(image).astype('float32') - 127.5) / 127.5\n",
        "    question = [\"[CLS]\"] + tokenizer.tokenize(question) + [\"[SEP]\"]\n",
        "    id = np.array(get_ids(question, tokenizer, max_seq_length)).reshape(1,128)\n",
        "    mask = np.array(get_masks(question, max_seq_length)).reshape(1,128)\n",
        "    segment = np.array(get_segments(question, max_seq_length)).reshape(1,128)\n",
        "    pred = model.predict([image,id,mask,segment])\n",
        "    return np.argmax(pred)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K7mCyM1X9uy0",
        "outputId": "ffe0c200-ac2b-4cf8-9f38-a64f41c4ffb2"
      },
      "source": [
        "from tqdm import tqdm\n",
        "import cv2\n",
        "import matplotlib.pyplot as plt\n",
        "!pip install fpdf\n",
        "from fpdf import FPDF\n",
        "\n",
        "import random\n",
        "def img(id,size):\n",
        "  f = '/content/images/val2014/'+'COCO_val2014_000000'+str(id)+'.jpg'\n",
        "  img = cv2.imread(f)\n",
        "  img = cv2.resize(img,(size,size))\n",
        "  img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)\n",
        "  return img,f\n",
        "\n",
        "def get_samples(p):\n",
        "  IMG_SIZE = 128\n",
        "  DATASET_SIZE = 30\n",
        "  x = 20000\n",
        "  a = 0\n",
        "  pdf = FPDF()\n",
        "  for j in tqdm(range(DATASET_SIZE)):\n",
        "    i = random.randint(0,40000)\n",
        "    img_id = d['annotations'][i]['image_id']\n",
        "    if len(str(img_id)) != 6:\n",
        "      zero = '0'*(6-len(str(img_id))) \n",
        "      img_id = zero + str(img_id)\n",
        "    image,f = img(img_id,IMG_SIZE)\n",
        "    image = image.reshape(1,128,128,3)\n",
        "    question = ques['questions'][i]['question']\n",
        "    answer = d['annotations'][i]['multiple_choice_answer']\n",
        "    output  = prediction(question,image)\n",
        "    answer_pred = ans_dict_inv[output]\n",
        "    if a%3 ==0:\n",
        "      pdf.add_page()\n",
        "    pdf.set_font(\"Arial\", size = 11)\n",
        "    pdf.cell(0, 7, txt = \"QUESTION : \"+question,\n",
        "        ln = 1, align = 'L')\n",
        "    pdf.cell(0, 7, txt = \"PREDICTION :\"+ answer_pred,\n",
        "        ln = 1, align = 'L')\n",
        "    pdf.cell(0, 7, txt = \"ANSWER :\"+ answer,\n",
        "        ln = 1, align = 'L')\n",
        "    pdf.image(f,x = 80, w=64,h=64)\n",
        "    a += 1\n",
        "\n",
        "  pdf.output(\"/content/drive/MyDrive/Colab Notebooks/vqa samples/samples\"+str(p)+\".pdf\")\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting fpdf\n",
            "  Downloading https://files.pythonhosted.org/packages/37/c6/608a9e6c172bf9124aa687ec8b9f0e8e5d697d59a5f4fad0e2d5ec2a7556/fpdf-1.7.2.tar.gz\n",
            "Building wheels for collected packages: fpdf\n",
            "  Building wheel for fpdf (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for fpdf: filename=fpdf-1.7.2-py2.py3-none-any.whl size=40722 sha256=133894d925bacedc0bebfaebb4eaa89f1a2d822f4f1e98bc1aae0867d4d39635\n",
            "  Stored in directory: /root/.cache/pip/wheels/9a/e9/77/4554ff5c99bc3f487c8d69620d8c41d99d54e9c54ab20ef4c9\n",
            "Successfully built fpdf\n",
            "Installing collected packages: fpdf\n",
            "Successfully installed fpdf-1.7.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 674
        },
        "id": "XmhB-ZIzegS7",
        "outputId": "067fb5f9-eb74-4717-8715-57ef284d6afa"
      },
      "source": [
        "import os\n",
        "for i in range(3):\n",
        "  model.fit([images,input_ids, input_masks, input_segments],y,epochs=3,batch_size=32)\n",
        "  get_samples(i)\n",
        "\n",
        "f = open('/content/drive/MyDrive/Colab Notebooks/vqa samples/vaq_model/model.h5')\n",
        "f.close()\n",
        "os.remove('/content/drive/MyDrive/Colab Notebooks/vqa samples/vaq_model/model.h5')\n",
        "model.save('/content/drive/MyDrive/Colab Notebooks/vqa samples/vaq_model/model.h5')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/3\n",
            "469/469 [==============================] - 561s 1s/step - loss: 3.9773 - accuracy: 0.2708\n",
            "Epoch 2/3\n",
            "469/469 [==============================] - 500s 1s/step - loss: 3.4048 - accuracy: 0.3877\n",
            "Epoch 3/3\n",
            "469/469 [==============================] - 499s 1s/step - loss: 3.0171 - accuracy: 0.4729\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 30/30 [00:06<00:00,  4.45it/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/3\n",
            "469/469 [==============================] - 500s 1s/step - loss: 2.6738 - accuracy: 0.5505\n",
            "Epoch 2/3\n",
            "469/469 [==============================] - 499s 1s/step - loss: 2.4086 - accuracy: 0.6025\n",
            "Epoch 3/3\n",
            "469/469 [==============================] - 499s 1s/step - loss: 2.1878 - accuracy: 0.6403\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 30/30 [00:03<00:00,  9.67it/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/3\n",
            " 23/469 [>.............................] - ETA: 7:56 - loss: 1.8914 - accuracy: 0.6943"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-30-491cce556ef7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m   \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mimages\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_masks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_segments\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m   \u001b[0mget_samples\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1181\u001b[0m                 _r=1):\n\u001b[1;32m   1182\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1183\u001b[0;31m               \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1184\u001b[0m               \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1185\u001b[0m                 \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    887\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    888\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jit_compile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 889\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    890\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    891\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    915\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    916\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 917\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    918\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    919\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   3022\u001b[0m        filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[1;32m   3023\u001b[0m     return graph_function._call_flat(\n\u001b[0;32m-> 3024\u001b[0;31m         filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[0m\u001b[1;32m   3025\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3026\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1959\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1960\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1961\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m   1962\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m   1963\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    594\u001b[0m               \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    595\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 596\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    597\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    598\u001b[0m           outputs = execute.execute_with_cancellation(\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 60\u001b[0;31m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m     61\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N0CZLT5RvHjh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d73ca18b-fa96-4853-b809-c68d40e46cd9"
      },
      "source": [
        "import os\n",
        "f = open('/content/drive/MyDrive/Colab Notebooks/vqa samples/vaq_model/model.h5')\n",
        "f.close()\n",
        "os.remove('/content/drive/MyDrive/Colab Notebooks/vqa samples/vaq_model/model.h5')\n",
        "model.save('/content/drive/MyDrive/Colab Notebooks/vqa samples/vaq_model/model.h5')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/utils/generic_utils.py:497: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n",
            "  category=CustomMaskWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HntTC1mMsyiJ",
        "outputId": "b9021a17-a19d-474a-8726-f988d59a1897"
      },
      "source": [
        "import os\n",
        "f = open('/content/drive/MyDrive/Colab Notebooks/vqa samples/vaq_model/model.h5')\n",
        "f.close()\n",
        "os.remove('/content/drive/MyDrive/Colab Notebooks/vqa samples/vaq_model/model.h5')\n",
        "model.save('/content/drive/MyDrive/Colab Notebooks/vqa samples/vaq_model/model.h5')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/utils/generic_utils.py:497: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n",
            "  category=CustomMaskWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PmvXzl8evK9j"
      },
      "source": [
        "from tqdm import tqdm\n",
        "import cv2\n",
        "import matplotlib.pyplot as plt\n",
        "correct = 0\n",
        "wrong = 0\n",
        "\n",
        "def img(id,size):\n",
        "  img = cv2.imread('/content/images/val2014/'+'COCO_val2014_000000'+str(id)+'.jpg')\n",
        "  img = cv2.resize(img,(size,size))\n",
        "  img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)\n",
        "  return img\n",
        "\n",
        "IMG_SIZE = 128\n",
        "DATASET_SIZE = 30\n",
        "x = 20000\n",
        "\n",
        "for i in tqdm(range(x,x+DATASET_SIZE)):\n",
        "  img_id = d['annotations'][i]['image_id']\n",
        "  if len(str(img_id)) != 6:\n",
        "    zero = '0'*(6-len(str(img_id))) \n",
        "    img_id = zero + str(img_id)\n",
        "  image = img(img_id,IMG_SIZE)\n",
        "  image = image.reshape(1,128,128,3)\n",
        "  question = ques['questions'][i]['question']\n",
        "  answer = d['annotations'][i]['multiple_choice_answer']\n",
        "  output  = prediction(question,image)\n",
        "  answer_pred = ans_dict_inv[output]\n",
        "  if answer_pred == answer:\n",
        "    correct += 1\n",
        "  else:\n",
        "    wrong += 1\n",
        "print('CORRECT : ',correct)\n",
        "print('WRONG ; ',wrong)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iVHGmwSwFU1B",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "94c7868e-112a-4b77-a1b3-76f65c7ddf8c"
      },
      "source": [
        "from tqdm import tqdm\n",
        "import cv2\n",
        "import matplotlib.pyplot as plt\n",
        "!pip install fpdf\n",
        "from fpdf import FPDF\n",
        "pdf = FPDF()\n",
        "import random\n",
        "def img(id,size):\n",
        "  f = '/content/images/val2014/'+'COCO_val2014_000000'+str(id)+'.jpg'\n",
        "  img = cv2.imread(f)\n",
        "  img = cv2.resize(img,(size,size))\n",
        "  img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)\n",
        "  return img,f\n",
        "\n",
        "\n",
        "\n",
        "def get_sample():\n",
        "  IMG_SIZE = 128\n",
        "  DATASET_SIZE = 30\n",
        "  x = 20000\n",
        "  a = 0\n",
        "  for j in tqdm(range(DATASET_SIZE)):\n",
        "    i = random.randint(0,40000)\n",
        "\n",
        "    img_id = d['annotations'][i]['image_id']\n",
        "    if len(str(img_id)) != 6:\n",
        "      zero = '0'*(6-len(str(img_id))) \n",
        "      img_id = zero + str(img_id)\n",
        "    image,f = img(img_id,IMG_SIZE)\n",
        "    image = image.reshape(1,128,128,3)\n",
        "    question = ques['questions'][i]['question']\n",
        "    answer = d['annotations'][i]['multiple_choice_answer']\n",
        "    output  = prediction(question,image)\n",
        "    answer_pred = ans_dict_inv[output]\n",
        "    if a%3 ==0:\n",
        "      pdf.add_page()\n",
        "    pdf.set_font(\"Arial\", size = 11)\n",
        "    pdf.cell(0, 7, txt = \"QUESTION : \"+question,\n",
        "        ln = 1, align = 'L')\n",
        "    pdf.cell(0, 7, txt = \"PREDICTION :\"+ answer_pred,\n",
        "        ln = 1, align = 'L')\n",
        "    pdf.cell(0, 7, txt = \"ANSWER :\"+ answer,\n",
        "        ln = 1, align = 'L')\n",
        "    pdf.image(f,x = 80, w=64,h=64)\n",
        "    a += 1\n",
        "\n",
        "  pdf.output(\"samples.pdf\")\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: fpdf in /usr/local/lib/python3.7/dist-packages (1.7.2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "22QzPS21Btpy",
        "outputId": "5163fa04-3bce-4b1b-da30-ec4027ea03dc"
      },
      "source": [
        "get_sample()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 30/30 [00:52<00:00,  1.75s/it]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0fjGbwo7L8-J",
        "outputId": "943248f7-3256-42fa-e22a-8a77f60a6a88"
      },
      "source": [
        "os.listdir('/content/drive/MyDrive/projects/vqa/models')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kMxrRg_5L_SE"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}