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import argparse
import pathlib
import json
from tqdm import tqdm
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim
from load_aokvqa import load_aokvqa
def map_to_choices(dataset, predictions, device='cpu'):
if isinstance(dataset, list):
dataset = { dataset[i]['question_id'] : dataset[i] for i in range(len(dataset)) }
if all([p in dataset[q]['choices'] for q, p in predictions.items()]):
return predictions
model = SentenceTransformer('sentence-transformers/average_word_embeddings_glove.6B.300d')
model.to(device)
for q in tqdm(predictions.keys()):
choices = dataset[q]['choices']
if predictions[q] not in choices:
choice_embeddings = model.encode([predictions[q]] + choices, convert_to_tensor=True)
a_idx = cos_sim(choice_embeddings[0], choice_embeddings[1:]).argmax().item()
predictions[q] = choices[a_idx]
return predictions
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--aokvqa-dir', type=pathlib.Path, required=True, dest='aokvqa_dir')
parser.add_argument('--split', type=str, choices=['train', 'val', 'test'], required=True)
parser.add_argument('--pred', type=argparse.FileType('r'), required=True, dest='prediction_file')
parser.add_argument('--out', type=argparse.FileType('w'), required=True, dest='output_file')
args = parser.parse_args()
dataset = load_aokvqa(args.aokvqa_dir, args.split)
predictions = json.load(args.prediction_file)
predictions = map_to_choices(dataset, predictions)
json.dump(predictions, args.output_file)
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