preprocess.py

import os
import sys
import json
import pickle

import nltk
import tqdm
from torchvision import transforms
from PIL import Image
from transforms import Scale


def process_question(root, out_dir, split, word_dic=None, answer_dic=None):
    if word_dic is None:
        word_dic = {}

    if answer_dic is None:
        answer_dic = {}

    with open(os.path.join(root, "questions", f"CLEVR_{split}_questions.json")) as f:
        data = json.load(f)

    result = []
    word_index = 1
    answer_index = 0

    for question in tqdm.tqdm(data["questions"]):
        words = nltk.word_tokenize(question["question"])
        question_token = []

        for word in words:
            try:
                question_token.append(word_dic[word])

            except:
                question_token.append(word_index)
                word_dic[word] = word_index
                word_index += 1

        answer_word = question["answer"]

        try:
            answer = answer_dic[answer_word]

        except:
            answer = answer_index
            answer_dic[answer_word] = answer_index
            answer_index += 1

        result.append(
            (question["image_filename"], question_token, answer, question["question_family_index"])
        )

    with open(os.path.join(out_dir, f"{split}.pkl"), "wb") as f:
        pickle.dump(result, f)

    return word_dic, answer_dic


if __name__ == "__main__":
    root = sys.argv[1]
    preprocessed_dir = os.path.join(root, "preprocessed")

    if not os.path.exists(preprocessed_dir):
        os.mkdir(preprocessed_dir)

    word_dic, answer_dic = process_question(root, preprocessed_dir, "train")
    process_question(root, preprocessed_dir, "val", word_dic, answer_dic)
    process_question(root, preprocessed_dir, "test", word_dic, answer_dic)

    with open(os.path.join(preprocessed_dir, "dic.pkl"), "wb") as f:
        pickle.dump({"word_dic": word_dic, "answer_dic": answer_dic}, f)