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import re
import json
import os
import torch
import torch.distributed as dist
import utils
def pre_caption(caption,max_words=50):
caption = re.sub(
r"([.!\"()*#:;~])",
' ',
caption.lower(),
)
caption = re.sub(
r"\s{2,}",
' ',
caption,
)
caption = caption.rstrip('\n')
caption = caption.strip(' ')
#truncate caption
caption_words = caption.split(' ')
if len(caption_words)>max_words:
caption = ' '.join(caption_words[:max_words])
return caption
def pre_question(question,max_ques_words=50):
question = re.sub(
r"([.!\"()*#:;~])",
'',
question.lower(),
)
question = question.rstrip(' ')
#truncate question
question_words = question.split(' ')
if len(question_words)>max_ques_words:
question = ' '.join(question_words[:max_ques_words])
return question
def save_result(result, result_dir, filename, remove_duplicate=''):
result_file = os.path.join(result_dir, '%s_rank%d.json'%(filename,utils.get_rank()))
final_result_file = os.path.join(result_dir, '%s.json'%filename)
json.dump(result,open(result_file,'w'))
dist.barrier()
if utils.is_main_process():
# combine results from all processes
result = []
for rank in range(utils.get_world_size()):
result_file = os.path.join(result_dir, '%s_rank%d.json'%(filename,rank))
res = json.load(open(result_file,'r'))
result += res
if remove_duplicate:
result_new = []
id_list = []
for res in result:
if res[remove_duplicate] not in id_list:
id_list.append(res[remove_duplicate])
result_new.append(res)
result = result_new
json.dump(result,open(final_result_file,'w'))
print('result file saved to %s'%final_result_file)
return final_result_file
from pycocotools.coco import COCO
from pycocoevalcap.eval import COCOEvalCap
from torchvision.datasets.utils import download_url
def coco_caption_eval(coco_gt_root, results_file, split):
urls = {'val':'https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_val_gt.json',
'test':'https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_test_gt.json'}
filenames = {'val':'coco_karpathy_val_gt.json','test':'coco_karpathy_test_gt.json'}
download_url(urls[split],coco_gt_root)
annotation_file = os.path.join(coco_gt_root,filenames[split])
# create coco object and coco_result object
coco = COCO(annotation_file)
coco_result = coco.loadRes(results_file)
# create coco_eval object by taking coco and coco_result
coco_eval = COCOEvalCap(coco, coco_result)
# evaluate on a subset of images by setting
# coco_eval.params['image_id'] = coco_result.getImgIds()
# please remove this line when evaluating the full validation set
# coco_eval.params['image_id'] = coco_result.getImgIds()
# evaluate results
# SPICE will take a few minutes the first time, but speeds up due to caching
coco_eval.evaluate()
# print output evaluation scores
for metric, score in coco_eval.eval.items():
print(f'{metric}: {score:.3f}')
return coco_eval |