pycocotools is not used

environment.yaml

@@ -55,8 +55,6 @@ dependencies:
     - open-clip-torch==2.0.2
     - pandas==1.4.3
     - piexif==1.1.3
-    - pycocotools==2.0.5
-    - pycocoevalcap==1.2
     - pudb==2019.2
     - pynvml==11.4.1
     - python-slugify>=6.1.2

ldm/data/utils.py

@@ -1,112 +0,0 @@
-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