Datasets:
Tasks:
Question Answering
Modalities:
Image
Formats:
imagefolder
Languages:
English
Size:
1K - 10K
License:
File size: 2,219 Bytes
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import json
import os
import tqdm
def preprocess():
all_train = json.load(open('trainval.json', 'r'))
im_ids = set()
for vqa_id, vqa in all_train.items():
im_ids.add(vqa['obs'][0])
move_to_mapping = dict()
subfolder_id = 0
subfolder_num_items = 0
max_items = 5000
for im_id in im_ids:
move_to_mapping[im_id] = subfolder_id
subfolder_num_items += 1
if subfolder_num_items >= max_items:
subfolder_id += 1
subfolder_num_items = 0
json.dump(move_to_mapping, open('move_to_mapping.json', 'w'), indent=2)
print(len(im_ids))
def refactor():
new_root_folder = 'trainval_obs'
if not os.path.exists(new_root_folder):
os.makedirs(new_root_folder)
move_to_mapping = json.load(open('move_to_mapping.json', 'r'))
all_train = json.load(open('trainval.json', 'r'))
for vqa_id, vqa in tqdm.tqdm(all_train.items(), total=len(all_train)):
im_id = vqa['obs'][0]
im_file_name = os.path.basename(im_id)
subfolder_id = move_to_mapping[im_id]
subfolder_path = os.path.join(new_root_folder, str(subfolder_id))
new_im_path = os.path.join(subfolder_path, im_file_name)
if not os.path.exists(subfolder_path):
os.makedirs(subfolder_path)
if not os.path.exists(new_im_path):
#print(f'Moving {im_id} to {new_im_path}')
os.rename(im_id, new_im_path)
vqa['obs'] = [new_im_path]
json.dump(all_train, open('updated_trainval.json', 'w'), indent=2)
def sanity_check():
prev_vqas = json.load(open('trainval.json', 'r'))
now_vqas = json.load(open('updated_trainval.json', 'r'))
assert len(prev_vqas) == len(now_vqas), 'Number of VQAs should be the same after refactoring.'
for vqa_id, vqa in now_vqas.items():
assert prev_vqas[vqa_id]['obs'][0] != vqa['obs'][0], 'VQA obs should be updated after refactoring.'
old_im_name = os.path.basename(prev_vqas[vqa_id]['obs'][0])
new_im_name = os.path.basename(vqa['obs'][0])
assert old_im_name == new_im_name, 'Image file names should be the same'
if __name__ == '__main__':
pass
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