def prepare_train_data(config):
""" Prepare the data for training the model. """
vqa = VQA(config.train_answer_file, config.train_question_file)
vqa.filter_by_ques_len(config.max_question_length)
vqa.filter_by_ans_len(1)
print("Reading the questions and answers...")
annotations = process_vqa(vqa, 'COCO_train2014', config.train_image_dir,
config.temp_train_annotation_file)
image_files = annotations['image_file'].values
questions = annotations['question'].values
question_ids = annotations['question_id'].values
answers = annotations['answer'].values
print("Questions and answers read.")
print("Number of questions = %d" % (len(question_ids)))
print("Building the vocabulary...")
vocabulary = Vocabulary()
if not os.path.exists(config.vocabulary_file):
for question in tqdm(questions):
vocabulary.add_words(word_tokenize(question))
for answer in tqdm(answers):
vocabulary.add_words(word_tokenize(answer))
vocabulary.compute_frequency()
vocabulary.save(config.vocabulary_file)
else:
vocabulary.load(config.vocabulary_file)
print("Vocabulary built.")
print("Number of words = %d" % (vocabulary.size))
config.vocabulary_size = vocabulary.size
print("Processing the questions and answers...")
if not os.path.exists(config.temp_train_data_file):
question_word_idxs, question_lens = process_questions(
questions, vocabulary, config)
answer_idxs = process_answers(answers, vocabulary)
data = {
'question_word_idxs': question_word_idxs,
'question_lens': question_lens,
'answer_idxs': answer_idxs
}
np.save(config.temp_train_data_file, data)
else:
data = np.load(config.temp_train_data_file).item()
question_word_idxs = data['question_word_idxs']
question_lens = data['question_lens']
answer_idxs = data['answer_idxs']
print("Questions and answers processed.")
print("Building the dataset...")
dataset = DataSet(image_files, question_word_idxs, question_lens,
question_ids, config.batch_size, answer_idxs, True, True)
print("Dataset built.")
return dataset, config
def build_vocabulary(config):
""" Build the vocabulary from the training data and save it to a file. """
vqa = VQA(config.train_answer_file, config.train_question_file)
vqa.filter_by_ques_len(config.max_question_length)
vqa.filter_by_ans_len(1)
question_ids = list(vqa.qa.keys())
questions = [vqa.qqa[k]['question'] for k in question_ids]
answers = [vqa.qa[k]['best_answer'] for k in question_ids]
vocabulary = Vocabulary()
for question in tqdm(questions):
vocabulary.add_words(word_tokenize(question))
for answer in tqdm(answers):
vocabulary.add_words(word_tokenize(answer))
vocabulary.compute_frequency()
vocabulary.save(config.vocabulary_file)
return vocabulary
