Spaces:
Runtime error
Runtime error
File size: 3,920 Bytes
15b0eda |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 |
import argparse
import json
import os
import re
import random
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--base-dir', type=str)
parser.add_argument('--result-file', type=str)
parser.add_argument('--output-file', type=str)
parser.add_argument('--output-result', type=str)
parser.add_argument('--split', type=str, default='test')
parser.add_argument('--options', type=list, default=["A", "B", "C", "D", "E"])
return parser.parse_args()
def convert_caps(results):
fakecaps = []
for result in results:
image_id = result['question_id']
caption = result['text']
fakecaps.append({"image_id": int(image_id), "caption": caption})
return fakecaps
def get_pred_idx(prediction, choices, options):
"""
Get the index (e.g. 2) from the prediction (e.g. 'C')
"""
if prediction in options[:len(choices)]:
return options.index(prediction)
else:
return -1
return random.choice(range(len(choices)))
if __name__ == "__main__":
args = get_args()
base_dir = args.base_dir
split_indices = json.load(open(os.path.join(base_dir, "pid_splits.json")))[args.split]
problems = json.load(open(os.path.join(base_dir, "problems.json")))
predictions = [json.loads(line) for line in open(args.result_file)]
predictions = {pred['question_id']: pred for pred in predictions}
split_problems = {idx: problems[idx] for idx in split_indices}
results = {'correct': [], 'incorrect': []}
sqa_results = {}
sqa_results['acc'] = None
sqa_results['correct'] = None
sqa_results['count'] = None
sqa_results['results'] = {}
sqa_results['outputs'] = {}
for prob_id, prob in split_problems.items():
if prob_id not in predictions:
pred = {'text': 'FAILED', 'prompt': 'Unknown'}
pred_text = 'FAILED'
else:
pred = predictions[prob_id]
pred_text = pred['text']
if pred_text in args.options:
answer = pred_text
elif len(pred_text) >= 3 and pred_text[0] in args.options and pred_text[1:3] == ". ":
answer = pred_text[0]
else:
pattern = re.compile(r'The answer is ([A-Z]).')
res = pattern.findall(pred_text)
if len(res) == 1:
answer = res[0] # 'A', 'B', ...
else:
answer = "FAILED"
pred_idx = get_pred_idx(answer, prob['choices'], args.options)
analysis = {
'question_id': prob_id,
'parsed_ans': answer,
'ground_truth': args.options[prob['answer']],
'question': pred['prompt'],
'pred': pred_text,
'is_multimodal': '<image>' in pred['prompt'],
}
sqa_results['results'][prob_id] = get_pred_idx(answer, prob['choices'], args.options)
sqa_results['outputs'][prob_id] = pred_text
if pred_idx == prob['answer']:
results['correct'].append(analysis)
else:
results['incorrect'].append(analysis)
correct = len(results['correct'])
total = len(results['correct']) + len(results['incorrect'])
###### IMG ######
multimodal_correct = len([x for x in results['correct'] if x['is_multimodal']])
multimodal_incorrect = len([x for x in results['incorrect'] if x['is_multimodal']])
multimodal_total = multimodal_correct + multimodal_incorrect
###### IMG ######
print(f'Total: {total}, Correct: {correct}, Accuracy: {correct / total * 100:.2f}%, IMG-Accuracy: {multimodal_correct / multimodal_total * 100:.2f}%')
sqa_results['acc'] = correct / total * 100
sqa_results['correct'] = correct
sqa_results['count'] = total
with open(args.output_file, 'w') as f:
json.dump(results, f, indent=2)
with open(args.output_result, 'w') as f:
json.dump(sqa_results, f, indent=2)
|