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Running
on
Zero
Running
on
Zero
File size: 1,309 Bytes
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import os
import json
import numpy as np
import argparse
import pathlib
from collections import Counter
from load_aokvqa import load_aokvqa
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('--mc', action='store_true', dest='multiple_choice')
parser.add_argument('--out', type=argparse.FileType('w'), required=True, dest='output_file')
args = parser.parse_args()
np.random.seed(0)
train_set = load_aokvqa(args.aokvqa_dir, 'train')
train_freq = dict(Counter(
[d['choices'][d['correct_choice_idx']] for d in train_set]
))
if args.multiple_choice is False:
choices = list(train_freq.keys())
probs = [f / len(train_set) for f in train_freq.values()]
##
predictions = {}
eval_set = load_aokvqa(args.aokvqa_dir, args.split)
for d in eval_set:
if args.multiple_choice:
choices = d['choices']
probs = [train_freq.get(c, 0) for c in choices]
if probs == [0, 0, 0, 0]:
probs = [1, 1, 1, 1]
probs = [p / sum(probs) for p in probs]
q = d['question_id']
predictions[q] = np.random.choice(choices, size=1, p=probs)[0]
json.dump(predictions, args.output_file)
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