prepare_data.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yMq0LIOSqLsx"
      },
      "source": [
        "### Flickr30\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [],
      "source": [
        "import torch\n",
        "import pickle\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "from data_utils import Flickr30\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [],
      "source": [
        "Flickr30_image_path = 'ImgCap/data/Flickr30/imges'\n",
        "Flickr30_labels_path = 'ImgCap/data/Flickr30/results.csv'\n",
        "\n",
        "with open(\"ImgCap/vocab.pkl\", 'rb') as f:\n",
        "        vocab = pickle.load(f)\n",
        "\n",
        "Flickr30_DataSet = Flickr30(Flickr30_image_path, Flickr30_labels_path, vocab=vocab)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([   2, 3390, 2884, 1613,   12, 4095, 4087, 4076, 4073, 4086, 4077, 4074,\n",
              "        4095, 4069, 4076, 4073, 4081, 4072,   10, 1210,  634, 1417,    6,    3])"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "examble = \"Hello my name is sherif ahemd and I can fly.\"\n",
        "tokens = Flickr30_DataSet.encoder(examble)\n",
        "tokens"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(24, 44)"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(tokens), len(examble)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'<sos> hello my name is sherif ahemd and i can fly .<eos>'"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Flickr30_DataSet.decoder(tokens)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "' shooting'"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Flickr30_DataSet.vocab.get_itos()[1023]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "32"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Flickr30_DataSet.vocab.get_stoi()[' girl']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "4096"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(Flickr30_DataSet.vocab)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [],
      "source": [
        "fig, ax = plt.subplots(2, 2, figsize=(40, 20)) \n",
        "samples = torch.randint(len(Flickr30_DataSet), (4, ))  \n",
        "\n",
        "for i , idx in enumerate(samples.tolist()):\n",
        "    i, j = i//2 , i%2\n",
        "    ax[i][j].imshow(Flickr30_DataSet[idx][0])\n",
        "    caption = Flickr30_DataSet.decoder(Flickr30_DataSet[idx][1])\n",
        "    ax[i][j].set_title(caption)\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from eval_utils import eval_bleu_score , eval_CIDEr"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "references = [\"this is a test\", \"this is another test\"]\n",
        "candidates = [\"this is a test\", \"this is another test\"]\n",
        "\n",
        "eval_bleu_score(references=references, candidates=candidates)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "references = [\"this is a test\", \"this is another test\"]\n",
        "candidates = [\"this is a test\", \"this is another test\"]\n",
        "\n",
        "vg_score, scores = eval_CIDEr(references=references, candidates=candidates)\n",
        "vg_score"
      ]
    }
  ],
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