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import os
# from pycocotools.coco import COCO
# from pycocoevalcap.eval import COCOEvalCap
from .coco_caption.pycocotools.coco import COCO
from .coco_caption.pycocoevalcap.eval import COCOEvalCap
import torch
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
def uit_viic_caption_eval(gt, results_file, split):
# create cap object and cap_result object
files = {
'val': 'uitviic_captions_val2017.json',
'test': 'uitviic_captions_test2017.json'
}
gt_file = os.path.join(gt, files[split])
cap = COCO(gt_file)
cap_result = cap.loadRes(results_file)
# create cap_eval object by taking cap and cap_result
cap_eval = COCOEvalCap(cap, cap_result)
# evaluate on a subset of images by setting
# cap_eval.params['image_id'] = cap_result.getImgIds()
# please remove this line when evaluating the full validation set
# cap_eval.params['image_id'] = cap_result.getImgIds()
# evaluate results
# SPICE will take a few minutes the first time, but speeds up due to caching
cap_eval.evaluate()
# print output evaluation scores
for metric, score in cap_eval.eval.items():
print(f'{metric}: {score:.3f}')
return cap_eval |