LlavaMistral1 / llava /eval /eval_science_qa_gpt4_requery.py
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import argparse
import json
import os
import re
import random
from collections import defaultdict
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--base-dir', type=str)
parser.add_argument('--gpt4-result', type=str)
parser.add_argument('--requery-result', type=str)
parser.add_argument('--our-result', 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 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")))
our_predictions = [json.loads(line) for line in open(args.our_result)]
our_predictions = {pred['question_id']: pred for pred in our_predictions}
split_problems = {idx: problems[idx] for idx in split_indices}
requery_predictions = [json.loads(line) for line in open(args.requery_result)]
requery_predictions = {pred['question_id']: pred for pred in requery_predictions}
gpt4_predictions = json.load(open(args.gpt4_result))['outputs']
results = defaultdict(lambda: 0)
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 our_predictions:
assert False
if prob_id not in gpt4_predictions:
assert False
our_pred = our_predictions[prob_id]['text']
gpt4_pred = gpt4_predictions[prob_id]
if prob_id not in requery_predictions:
results['missing_requery'] += 1
requery_pred = "MISSING"
else:
requery_pred = requery_predictions[prob_id]['text']
pattern = re.compile(r'The answer is ([A-Z]).')
our_res = pattern.findall(our_pred)
if len(our_res) == 1:
our_answer = our_res[0] # 'A', 'B', ...
else:
our_answer = "FAILED"
requery_res = pattern.findall(requery_pred)
if len(requery_res) == 1:
requery_answer = requery_res[0] # 'A', 'B', ...
else:
requery_answer = "FAILED"
gpt4_res = pattern.findall(gpt4_pred)
if len(gpt4_res) == 1:
gpt4_answer = gpt4_res[0] # 'A', 'B', ...
else:
gpt4_answer = "FAILED"
our_pred_idx = get_pred_idx(our_answer, prob['choices'], args.options)
gpt4_pred_idx = get_pred_idx(gpt4_answer, prob['choices'], args.options)
requery_pred_idx = get_pred_idx(requery_answer, prob['choices'], args.options)
results['total'] += 1
if gpt4_answer == 'FAILED':
results['gpt4_failed'] += 1
if gpt4_pred_idx == prob['answer']:
results['gpt4_correct'] += 1
if our_pred_idx == prob['answer']:
results['gpt4_ourvisual_correct'] += 1
elif gpt4_pred_idx == prob['answer']:
results['gpt4_correct'] += 1
results['gpt4_ourvisual_correct'] += 1
if our_pred_idx == prob['answer']:
results['our_correct'] += 1
if requery_answer == 'FAILED':
sqa_results['results'][prob_id] = our_pred_idx
if our_pred_idx == prob['answer']:
results['requery_correct'] += 1
else:
sqa_results['results'][prob_id] = requery_pred_idx
if requery_pred_idx == prob['answer']:
results['requery_correct'] += 1
else:
print(f"""
Question ({args.options[prob['answer']]}): {our_predictions[prob_id]['prompt']}
Our ({our_answer}): {our_pred}
GPT-4 ({gpt4_answer}): {gpt4_pred}
Requery ({requery_answer}): {requery_pred}
print("=====================================")
""")
if gpt4_pred_idx == prob['answer'] or our_pred_idx == prob['answer']:
results['correct_upperbound'] += 1
total = results['total']
print(f'Total: {total}, Our-Correct: {results["our_correct"]}, Accuracy: {results["our_correct"] / total * 100:.2f}%')
print(f'Total: {total}, GPT-4-Correct: {results["gpt4_correct"]}, Accuracy: {results["gpt4_correct"] / total * 100:.2f}%')
print(f'Total: {total}, GPT-4 NO-ANS (RANDOM): {results["gpt4_failed"]}, Percentage: {results["gpt4_failed"] / total * 100:.2f}%')
print(f'Total: {total}, GPT-4-OursVisual-Correct: {results["gpt4_ourvisual_correct"]}, Accuracy: {results["gpt4_ourvisual_correct"] / total * 100:.2f}%')
print(f'Total: {total}, Requery-Correct: {results["requery_correct"]}, Accuracy: {results["requery_correct"] / total * 100:.2f}%')
print(f'Total: {total}, Correct upper: {results["correct_upperbound"]}, Accuracy: {results["correct_upperbound"] / total * 100:.2f}%')
sqa_results['acc'] = results["requery_correct"] / total * 100
sqa_results['correct'] = results["requery_correct"]
sqa_results['count'] = total
with open(args.output_result, 'w') as f:
json.dump(sqa_results, f, indent=2)