MiniGPT-4/data/create_textvqa_dataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "BASE_DIR = Path(\"./TextVQA\")\n",
    "NEW_DIR = Path(\"./TextVQA_tiny\")\n",
    "NEW_DIR.mkdir(exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "BASE_IMG_DIR = BASE_DIR / \"train_images\"\n",
    "NEW_IMG_DIR = NEW_DIR / \"images\"\n",
    "NEW_IMG_DIR.mkdir(exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. READ and FILTER the answer for each question"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "from typing import List\n",
    "from collections import Counter\n",
    "\n",
    "\n",
    "def filter_answers(answers: List[str]) -> str:\n",
    "    \"\"\"\n",
    "    Filter out answers that are most common in a list of answers\n",
    "    :param answers: List of answers\n",
    "    :return: Most common answer\n",
    "    \"\"\"\n",
    "    frequency = Counter(answers)\n",
    "    max_freq = max(frequency.values())\n",
    "    tie_terms = [term for term, count in frequency.items() if count == max_freq]\n",
    "    if len(tie_terms) == 0:\n",
    "        return None\n",
    "    if \"unanswerable\" in tie_terms:\n",
    "        if len(tie_terms) > 1:\n",
    "            tie_terms.remove(\"unanswerable\")\n",
    "            return tie_terms[0]\n",
    "        else:\n",
    "            return None\n",
    "    else:\n",
    "        random_term = random.choice(tie_terms)\n",
    "        return random_term"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(34120, 4922)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_fields = [\"question\", \"image_id\", \"image_width\", \"image_height\", \"answers\"]\n",
    "\n",
    "with open(BASE_DIR / \"TextVQA_0.5.1_train.json\", \"r\") as f:\n",
    "    train_set = json.load(f)[\"data\"]\n",
    "    train_set = [{k: v for k, v in d.items() if k in target_fields} for d in train_set]\n",
    "    for d in train_set:\n",
    "        filtered_answer = filter_answers(d[\"answers\"])\n",
    "        if filtered_answer is None:\n",
    "            train_set.remove(d)\n",
    "        else:\n",
    "            d[\"answer\"] = filter_answers(d[\"answers\"])\n",
    "\n",
    "with open(BASE_DIR / \"TextVQA_0.5.1_val.json\", \"r\") as f:\n",
    "    val_set = json.load(f)[\"data\"]\n",
    "    val_set = [{k: v for k, v in d.items() if k in target_fields} for d in val_set]\n",
    "    for d in val_set:\n",
    "        filtered_answer = filter_answers(d[\"answers\"])\n",
    "        if filtered_answer is None:\n",
    "            val_set.remove(d)\n",
    "        else:\n",
    "            d[\"answer\"] = filter_answers(d[\"answers\"])\n",
    "        \n",
    "\n",
    "len(train_set), len(val_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "random.seed(42)\n",
    "random.shuffle(train_set)\n",
    "random.shuffle(val_set)\n",
    "\n",
    "train_set = train_set[:4000]\n",
    "val_set = val_set[:1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "for d in train_set:\n",
    "    img_id = d[\"image_id\"]\n",
    "    os.system(f\"cp {BASE_IMG_DIR}/{img_id}.jpg {NEW_IMG_DIR}/{img_id}.jpg\")\n",
    "\n",
    "for d in val_set:\n",
    "    img_id = d[\"image_id\"]\n",
    "    os.system(f\"cp {BASE_IMG_DIR}/{img_id}.jpg {NEW_IMG_DIR}/{img_id}.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(NEW_DIR / \"train.json\", \"w\") as f:\n",
    "    json.dump(train_set, f, indent=4)\n",
    "\n",
    "with open(NEW_DIR / \"val.json\", \"w\") as f:\n",
    "    json.dump(val_set, f, indent=4)\n",
    "    "
   ]
  }
 ],
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