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https://github.com/Vision-CAIR/MiniGPT-4.git
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46 lines
1.2 KiB
Python
46 lines
1.2 KiB
Python
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import os
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import argparse
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from collections import Counter
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import pathlib
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from load_aokvqa import load_aokvqa
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parser = argparse.ArgumentParser()
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parser.add_argument('--aokvqa-dir', type=pathlib.Path, required=True, dest='aokvqa_dir')
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parser.add_argument('--out', type=pathlib.Path, required=True, dest='output_file')
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args = parser.parse_args()
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# Build vocab from train set: correct choices + (direct answers appearing in >= 3 )
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train_set = load_aokvqa(args.aokvqa_dir, 'train')
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vocab = []
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all_choices = Counter()
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direct_answers = Counter()
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for i in train_set:
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vocab.append( i['choices'][i['correct_choice_idx']] )
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all_choices.update(i['choices'])
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direct_answers.update(set(i['direct_answers']))
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vocab += [k for k,v in all_choices.items() if v >= 3]
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vocab += [k for k,v in direct_answers.items() if v >= 3]
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vocab = sorted(set(vocab))
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print(f"Vocab size: {len(vocab)}")
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# Save vocabulary Output
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with open(args.output_file, 'w') as f:
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for v in vocab:
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print(v, file=f)
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## Check validation set coverage
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val_set = load_aokvqa(args.aokvqa_dir, 'val')
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val_acc = [v['choices'][v['correct_choice_idx']] in vocab for v in val_set]
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val_acc = sum(val_acc) / len(val_acc) * 100
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print(f"Val set coverage: {val_acc:.2f}" )
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