def __init__(self,cfg,path,voc):
self.cfg = cfg
self.voc = voc
self.path = path
self.data_transform = transforms.Compose([transforms.Resize((224,224)),transforms.ToTensor()])
self.coco_train = dset.CocoCaptions(root=self.path.train_image_path,annFile=self.path.train_annotation_file,transform=self.data_transform)
self.coco_val = dset.CocoCaptions(root=self.path.val_image_path,annFile=self.path.val_annotation_file,transform=self.data_transform)
self.id2fname = {}
test_info = json.load(open(self.path.test_info_path))
for img in test_info['images']:
self.id2fname[img['id']] = img['file_name']
def get_coco_data(vocab,
train=True,
img_size=224,
scale_size=256,
normalize=__normalize):
if train:
root, annFile = __TRAIN_PATH['root'], __TRAIN_PATH['annFile']
img_transform = transforms.Compose([
transforms.Scale(scale_size),
transforms.RandomCrop(img_size),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(**normalize)
])
else:
root, annFile = __VAL_PATH['root'], __VAL_PATH['annFile']
img_transform = transforms.Compose([
transforms.Scale(scale_size),
transforms.CenterCrop(img_size),
transforms.ToTensor(),
transforms.Normalize(**normalize)
])
data = (dset.CocoCaptions(root=root,
annFile=annFile,
transform=img_transform,
target_transform=create_target(vocab,
train)), vocab)
return data
def prepare(self):
self.word2idx = defaultdict(int)
# to make sure start_symbol, end_symbol, pad, and unk will be included
self.word2idx[self.START_SYMBOL] = self.word2idx[
self.END_SYMBOL] = self.word2idx[self.UNK] = self.word2idx[
self.PAD] = self.min_word_freq
for dataset_type in ["train", "val"]:
caps = dset.CocoCaptions(
root=FilePathManager.resolve(f'data/{dataset_type}'),
annFile=FilePathManager.resolve(
f"data/annotations/captions_{dataset_type}2017.json"),
transform=transforms.ToTensor())
for _, captions in caps:
for capt in captions:
tokens = self.tokenize(capt)
for token in tokens:
self.word2idx[token] += 1
temp = {}
embeddings = {}
fast_text = FastText.load(
FilePathManager.resolve("data/fasttext.model"), mmap="r")
for k, v in self.word2idx.items():
if v >= self.min_word_freq:
temp[k] = len(temp)
embeddings[k] = fast_text[k] if k in fast_text else fast_text[
self.UNK]
self.word2idx = temp
# swap keys and values
self.idx2word = dict(zip(self.word2idx.values(), self.word2idx.keys()))
self.fast_text = embeddings
def extract_coco_feature(model, batch_size=64):
transform = transforms.Compose([
transforms.Resize(256),
transforms.RandomCrop(224, pad_if_needed=True),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
data = datasets.CocoCaptions('./data/train2014',
'./data/annotations/captions_train2014.json',
transform=transform)
loader = dataloader.DataLoader(data, batch_size=batch_size)
model.eval()
features = []
captions = []
length = len(loader)
for i, (images, captions) in enumerate(loader):
if torch.cuda.is_available():
images = images.cuda()
feature = model(images)
features.append(feature.cpu())
captions.extend(captions[0])
if i % 100 == 0:
print('[%d/%d] finished' % (i, length))
return torch.cat(features, 0), captions
def __init__(self, images, annotations, augment, normalize=True):
"""
Initializes the dataset.
Parameters:
images:
The path of the directory that contains the images
annotations:
The path of the directory that contains the captions
augment:
Whether to use data augmentation techniques
normalize:
Whether to normalize the images
"""
trans = transforms.Compose([_imageAugment, _imagePrep
]) if augment else _imagePrep
if normalize:
trans = transforms.Compose([
trans,
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
self.coco = datasets.CocoCaptions(root=images,
annFile=annotations,
transform=trans)
def dataloader(image_folder, captions_file, batch_size, stoi):
spell = SpellChecker(distance=1)
#Definir el tensor para guardar las imagenes de un batch
tensor_images = torch.zeros((batch_size, 3, 224, 224)).to(device)
#Definir las transformaciones que se aplican a las imagenes
transform = transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229,0.224, 0.225])])
#Cargar el dataset
cap = datasets.CocoCaptions(root = image_folder,
annFile = captions_file,
transform = transform)
for batch in range(int( len(cap)/batch_size )):
# for batch in range(15):
captions = []
for i in range(batch_size):
#Obtener una imagen con sus captions y seleccionar uno al azar
img, target = cap[batch*batch_size+i]
# Pasar la frase a todo minusculas y separar signos de puntuacion
captions.append([word if word in stoi else spell.correction(word) for word in re.findall(r"[\w']+|[.,!?;]", target[random.randrange(5)].lower())])
#Actualizar el tensor de imagenes
tensor_images[i] = img
yield tensor_images, captions
def get_dataset(data_set):
assert data_set in ['train', 'val', 'test']
data_root = f'cocoapi/images/{data_set}2017/'
annfile = f'cocoapi/annotations/captions_{data_set}2017.json'
dataset = dset.CocoCaptions(root=data_root,
annFile=annfile,
transform=picture_tranform_func)
return dataset
def __init__(self, corpus: Corpus, transform=None, captions_per_image=2):
self.corpus = corpus
self.captions = dset.CocoCaptions(
root=FilePathManager.resolve(f'data/train'),
annFile=FilePathManager.resolve(
f"data/annotations/captions_train2017.json"),
transform=transform)
self.captions_per_image = captions_per_image
def __init__(self, corpus: Corpus, evaluator: bool = True, transform=None):
self.corpus = corpus
self.evaluator = evaluator
self.captions = dset.CocoCaptions(
root=FilePathManager.resolve(f'data/train'),
annFile=FilePathManager.resolve(
f"data/annotations/captions_train2017.json"),
transform=transform)
def __init__(self, tranform=None):
self.captions = dset.CocoCaptions(
root=FilePathManager.resolve(f'data/train'),
annFile=FilePathManager.resolve(
f"data/annotations/captions_train2017.json"),
transform=tranform)
self.length = len(self.captions)
self.s = set(range(self.length))
def __init__(self, corpus: Corpus):
self.corpus = corpus
self.captions = dset.CocoCaptions(root=FilePathManager.resolve(f'data/train'),
annFile=FilePathManager.resolve(
f"data/annotations/captions_train2017.json"),
transform=transforms.ToTensor())
with open(FilePathManager.resolve("data/embedded_images.pkl"), "rb") as f:
self.images = pickle.load(f)
self.length = len(self.images) * 5
def get_val_loader(root, annFile, transform, shuffle=False, num_workers=8):
val_set = datasets.CocoCaptions(root,
annFile,
transform=transform,
target_transform=None)
val_set_loader = torch.utils.data.DataLoader(val_set,
batch_size=1,
shuffle=shuffle,
num_workers=num_workers)
return val_set_loader
def __init__(self, root, img_transform=imagenet_transform,
split='train',
tokenization='bpe',
num_symbols=32000,
shared_vocab=True,
code_file=None,
vocab_file=None,
insert_start=[BOS], insert_end=[EOS],
mark_language=False,
tokenizer=None,
sample_caption=True):
super(CocoCaptions, self).__init__()
self.shared_vocab = shared_vocab
self.num_symbols = num_symbols
self.tokenizer = tokenizer
self.tokenization = tokenization
self.insert_start = insert_start
self.insert_end = insert_end
self.mark_language = mark_language
self.code_file = code_file
self.vocab_file = vocab_file
self.sample_caption = None
self.img_transform = img_transform
if split == 'train':
path = {'root': os.path.join(root, 'train2014'),
'annFile': os.path.join(root, 'annotations/captions_train2014.json')
}
if sample_caption:
self.sample_caption = randrange
else:
path = {'root': os.path.join(root, 'val2014'),
'annFile': os.path.join(root, 'annotations/captions_val2014.json')
}
if sample_caption:
self.sample_caption = lambda l: 0
self.data = dset.CocoCaptions(root=path['root'], annFile=path[
'annFile'], transform=img_transform(train=(split == 'train')))
if self.tokenizer is None:
prefix = os.path.join(root, 'coco')
if tokenization not in ['bpe', 'char', 'word']:
raise ValueError("An invalid option for tokenization was used, options are {0}".format(
','.join(['bpe', 'char', 'word'])))
if tokenization == 'bpe':
self.code_file = code_file or '{prefix}.{lang}.{tok}.codes_{num_symbols}'.format(
prefix=prefix, lang='en', tok=tokenization, num_symbols=num_symbols)
else:
num_symbols = ''
self.vocab_file = vocab_file or '{prefix}.{lang}.{tok}.vocab{num_symbols}'.format(
prefix=prefix, lang='en', tok=tokenization, num_symbols=num_symbols)
self.generate_tokenizer()
def __init__(self, coco_path, annFile, batch_size):
self.coco_data = datasets.CocoCaptions(root=coco_path, annFile=annFile)
self.paths = np.arange(len(self.coco_data))
self.index = 0
self.batch_size = batch_size
self.init_count = 0
self.lock = threading.Lock() #mutex for input path
self.yield_lock = threading.Lock(
) #mutex for generator yielding of batch
self.path_id_generator = threadsafe_iter(get_path_i(len(self.paths)))
self.cumulative_batch = []
def get_data_loader(image_folder, annotation_file, batch_size=32):
"""
Get the torch data loader for the COCO dataset
:param image_folder: The folder containing the COCO images
:param annotation_file: The .json file for the image annotations
:param batch_size: The batch size
:return: A COCO captioning data loader
"""
# Build the torch dataset
coco_dataset = torchdata.CocoCaptions(image_folder, annotation_file)
# Return a dataloader object over this dataset
return DataLoader(coco_dataset, batch_size=batch_size)
def evaluate(image: str = None):
model = VisualBERT.load_from_checkpoint(
"models/electra/final-year-project/3osm0cr3/checkpoints/epoch=17-step=231535.ckpt",
manual_lm_head=True)
model.training_objective = TrainingObjective.Captioning
model.cuda()
model.eval()
feature_model = FeatureExtractor()
feature_model.cuda()
feature_model.eval()
if image is None:
data = CocoCaptions()
data.prepare_data()
data.setup()
images = "./data/raw/coco/train2017"
annotations = "./data/raw/coco/annotations/captions_train2017.json"
raw_dataset = tv.CocoCaptions(root=images,
annFile=annotations,
transform=load_image)
raw_subset = Subset(raw_dataset, data.val.indices)
dataloader = DataLoader(raw_subset, batch_size=16, num_workers=0)
# %%
batch = next(iter(dataloader))
images, caption_sets = batch
else:
image = PIL.Image.open(image)
images = load_image(image).unsqueeze(0).cuda()
caption_sets = []
fig = plt.figure()
num_rows = math.ceil(math.sqrt(images.shape[0]))
num_cols = num_rows
for i, image in enumerate(images.split(1)):
features = feature_model(image)[0]
features, mask = CocoCaptionsDataset.postprocess_features(features, 8)
features = features.unsqueeze(0)
mask = mask.unsqueeze(0)
generated = model.inference(features.cuda(), mask.cuda(), 20)
targets = [x[i] for x in caption_sets]
ax = fig.add_subplot(num_rows, num_cols, i + 1)
plt.imshow(image.cpu().squeeze().flip(2) / 255)
ax.set_xlabel(generated)
bleu = sentence_bleu([word_tokenize(x) for x in targets],
word_tokenize(generated))
print(f"{generated=}, {targets=}, {bleu}")
plt.show()
def createcaptiontxt():
coco_train = dset.CocoCaptions(
root='E:/datasets/coco2017/train2017',
annFile='E:/datasets/coco2017/annotations/captions_train2017.json',
transform=transforms.ToTensor())
print('Number of samples: ', len(coco_train))
f = open('cocotxt/caption.txt', 'w')
for i in tqdm(range(len(coco_train))):
_, target = coco_train[i]
f.write(''.join(target).replace("\n", ""))
f.write('\n')
print('create caption txt finish!')
def load_dataset(root,
transform,
*args,
batch_size=32,
shuffle=True,
dataset_type='folder',
**kwargs):
"""
Parameters
-----------
dataset_type: str
should be voc , coco, cifar, minst or folder
if you're using voc dataset then you have to pass a param as year = 2007 or 2012
if you're using coco dataset then you have to pass a param as type = 'detection' or 'caption'
Return
----------
data: Dataloader
dataset: torchvision.dataset
"""
if dataset_type == 'folder':
dataset = datasets.ImageFolder(root, transform=transform)
elif dataset_type == 'voc':
year = kwargs.get('year', 2007)
image_set = kwargs.get('image_set', 'train')
dataset = datasets.VOCDetection(root,
year=year,
image_set=image_set,
transform=transform)
elif dataset_type == 'coco':
assert 'type' in kwargs and 'annfile' in kwargs
annfile = kwargs['annfile']
type = kwargs['type']
if type == 'detection':
dataset = datasets.CocoDetection(root,
annFile=annfile,
transform=transform)
elif type == 'caption':
dataset = datasets.CocoCaptions(root,
annFile=annfile,
transform=transform)
data = DataLoader(dataset, batch_size=batch_size, shuffle=shuffle)
return data, dataset
def __init__(self, root, split, max_detections=50, sort_by_prob=False):
self.split = split
self.root = root
self.max_detections = max_detections
self.sort_by_prob = sort_by_prob
self.dset_captions = dset.CocoCaptions(
root='/netscratch/karayil/mscoco/data/' + split + '2014',
annFile="/netscratch/karayil/mscoco/data/annotations/captions_" +
split + "2014.json",
transform=transforms.ToTensor())
self.coco = self.dset_captions.coco
self.build_samples()
def extract_feats(args):
vgg = Vgg16()
utils.init_vgg16(args.vgg_model_dir)
cap = dset.CocoCaptions(
root='/Pulsar1/Datasets/coco/train2014/train2014',
annFile=
'/Neutron9/sahil.c/datasets/annotations/captions_train2014.json',
transform=transforms.ToTensor())
print('Number of samples: ', len(cap))
for i, t in cap:
image = i.unsqueeze(0)
image = Variable(utils.preprocess_batch(image), requires_grad=False)
image = utils.subtract_imagenet_mean_batch(image)
features_content = vgg(image)
def __init__(self,
data_dir,
ann_file,
embedding_type='cnn-rnn',
emb_model=None,
imsize=64,
transform=None,
target_transform=None):
self.transform = transform
self.target_transform = target_transform
self.imsize = imsize
self.glove = self.load_embedding(emb_model)
# if data_dir.find('birds') != -1:
# self.bbox = self.load_bbox()
# else:
# self.bbox = None
# split_dir = os.path.join(data_dir, split)
self.cap = dset.CocoCaptions(root=data_dir, annFile=ann_file)
self.idx2word = None
def main(args):
use_cuda = torch.cuda.is_available()
torch.manual_seed(random.randint(1, 10000))
device = torch.device("cuda" if use_cuda else "cpu")
kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
words = load_vocab()
vocab = {i: w for w, i in words.items()}
coco = datasets.CocoCaptions(args.root_dir, args.anno_path)
mycoco = MyCoco(
words,
args.root_dir,
args.anno_path,
transform=transforms.Compose([
transforms.Resize([args.im_size] * 2),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.407, 0.457, 0.485], # subtract imagenet mean
std=[1, 1, 1]),
]))
model = Captor(args.lr, args.weight_decay, args.lr_decay_rate, len(words),
args.embed_size)
model.to(device)
model.load_checkpoint(args.ckpt_path)
print('dataset length {}'.format(len(mycoco)))
score = 0
for i in range(len(mycoco)):
im, cap_enc = mycoco[i]
im_, caps = coco[i]
pred = beamsearch(model, device, im, vocab, return_sentence=False)
s = utils.bleu_score(utils.to_word_bags(caps), pred, n=args.bleu_n)
score = (score * i + s) / (i + 1)
print('processing {}th image... score: {:.2f}'.format(i, score),
flush=True,
end='\r')
print('\navg bleu score: {}'.format(score))
def get_coco_data_raw(train=True, img_size=224, scale_size=256):
if train:
root, annFile = __TRAIN_PATH['root'], __TRAIN_PATH['annFile']
img_transform = transforms.Compose([
transforms.Scale(scale_size),
transforms.RandomCrop(img_size),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
else:
root, annFile = __VAL_PATH['root'], __VAL_PATH['annFile']
img_transform = transforms.Compose([
transforms.Scale(scale_size),
transforms.CenterCrop(img_size),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
data = dset.CocoCaptions(root=root,
annFile=annFile,
transform=img_transform)
return data
def load_dataset(root,
transform,
batch_size=32,
shuffle=True,
dataset_type='folder',
*args,
**kwargs):
"""
param
dataset_type: str
should be voc , coco, cifar, minst or folder
"""
if dataset_type == 'folder':
dataset = datasets.ImageFolder(root, transform=transform)
elif dataset_type == 'voc':
year = kwargs['year']
image_set = kwargs['image_set']
dataset = datasets.VOCDetection(root,
year=year,
image_set=image_set,
transform=transform)
elif dataset_type == 'coco':
annfile = kwargs['annfile']
type = kwargs['type']
if type == 'detect':
dataset = datasets.CocoDetection(root,
annFile=annfile,
transform=transform)
elif type == 'caption':
dataset = datasets.CocoCaptions(root,
annFile=annfile,
transform=transform)
data = DataLoader(dataset, batch_size=batch_size, shuffle=shuffle)
return data, dataset.classes, dataset.class_to_idx
def getData(dset_name, batch_size, data_transform):
dataPath = "../data"
os.makedirs(dataPath, exist_ok=True)
if dset_name == "CIFAR10":
trainset = dset.CIFAR10(dataPath,
train=True,
download=True,
transform=data_transform)
testset = dset.CIFAR10(dataPath,
train=False,
download=True,
transform=data_transform)
elif dset_name == "LSUN":
trainset = dset.LSUN(dataPath,
train=True,
download=True,
transform=data_transform)
testset = dset.LSUN(dataPath,
train=False,
download=True,
transform=data_transform)
elif dset_name == "FakeData":
trainset = dset.FakeData(dataPath,
train=True,
download=True,
transform=data_transform)
testset = dset.FakeData(dataPath,
train=False,
download=True,
transform=data_transform)
elif dset_name == "CocoCaptions":
trainset = dset.CocoCaptions(dataPath,
train=True,
download=True,
transform=data_transform)
testset = dset.CocoCaptions(dataPath,
train=False,
download=True,
transform=data_transform)
elif dset_name == "MNIST":
trainset = dset.MNIST(dataPath,
train=True,
download=True,
transform=data_transform)
testset = dset.MNIST(dataPath,
train=False,
download=True,
transform=data_transform)
elif dset_name == "CIFAR100":
trainset = dset.CIFAR100(dataPath,
train=True,
download=True,
transform=data_transform)
testset = dset.CIFAR100(dataPath,
train=False,
download=True,
transform=data_transform)
elif dset_name == "SVHN":
trainset = dset.SVHN(dataPath,
train=True,
download=True,
transform=data_transform)
testset = dset.SVHN(dataPath,
train=False,
download=True,
transform=data_transform)
elif dset_name == "Flickr8k":
trainset = dset.Flickr8k(dataPath,
train=True,
download=True,
transform=data_transform)
testset = dset.Flickr8k(dataPath,
train=False,
download=True,
transform=data_transform)
elif dset_name == "Cityscapes":
trainset = dset.Cityscapes(dataPath,
train=True,
download=True,
transform=data_transform)
testset = dset.Cityscapes(dataPath,
train=False,
download=True,
transform=data_transform)
return torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True),len(trainset),\
torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=True), len(testset),
# Merge image tensors (stack)
images = torch.stack(images, 0)
# Merge captions
caption_lengths = [len(caption) for caption in captions]
# zero-matrix num_captions x caption_max_length
padded_captions = pad_sequence(captions, padding_value=0, batch_first=True)
return images, padded_captions, caption_lengths#, caption_lengths
if __name__ == '__main__':
root = './data/train2014'
annot = './data/annotations/captions_train2014.json'
coco = datasets.CocoCaptions(root, annot, transform=transforms.ToTensor())
words = word_dict(coco)
with open('./data/vocab.txt', 'w') as f:
for w in words:
f.write(w + '\n')
vocab = load_vocab('data/vocab.txt')
print(len(vocab))
print(len(coco))
im, enc = coco[5]
im = transforms.ToPILImage()(im)
im.show()
for i in range(len(coco)):
_ = coco[i]
sys.path.append('/home/xuxin/work/python/data/coco-master/PythonAPI')
import torchvision.datasets as dset
import torchvision.transforms as transforms
dataDir = '/home/xuxin/work/python/data'
imageDir = '%s/%s' % (dataDir, args.data)
capDir = '%s/Micro-coco/annotations/captions_%s.json' % (dataDir, args.data)
transforms = transforms.Compose([
transforms.Scale(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
])
cap = dset.CocoCaptions(root=imageDir, annFile=capDir, transform=transforms)
print type(cap)
print('Number of samples: ', len(cap))
########################################################################################################
'''
check one, give input
'''
########################################################################################################
import time
isLSTM = (args.mode == 'LSTM')
check_flag1 = False
if check_flag1:
criterion = nn.CrossEntropyLoss()
straight_through=
False # straight-through for gumbel softmax. unclear if it is better one way or the other
).cuda()
optimizerVAE = torch.optim.Adam(vae.parameters(), lr=learning_rate)
"""
text = torch.randint(0, NUM_TOKENS, (BATCH_SIZE, TEXTSEQLEN))
images = torch.randn(BATCH_SIZE, 3, IMAGE_SIZE, IMAGE_SIZE)
mask = torch.ones_like(text).bool()
"""
cap = dset.CocoCaptions(
root='./coco/images',
annFile='./coco/annotations/captions_val2014.json',
transform=transforms.Compose([
#transforms.RandomCrop((IMAGE_SIZE,IMAGE_SIZE),pad_if_needed=True),
#transforms.Grayscale(),
transforms.Resize((IMAGE_SIZE, IMAGE_SIZE), Image.BILINEAR),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]))
tokenDset = token_dataset('./coco/merged-1000.txt')
VAEloss = []
for epoch in range(EPOCHS):
for i in range(DATASET_SIZE):
#print(i,":",tokenDset.getRand(i),img.size())
optimizerVAE.zero_grad()
img, _ = cap[i]
img = img.unsqueeze(0).cuda()
import torchvision.datasets as dset
import torchvision.transforms as transforms
from JPtokenize import token_dataset
import torch
from dalle_pytorch import DiscreteVAE, DALLE
IMAGE_SIZE = 256
cap = dset.CocoCaptions(root='./coco/images',
annFile='./coco/annotations/captions_val2014.json',
transform=transforms.Compose([
transforms.Resize((IMAGE_SIZE, IMAGE_SIZE)),
transforms.ToTensor(),
]))
tokenDset = token_dataset('./coco/merged.txt')
print('Number of samples: ', len(cap))
#img, target = cap[3] # load 4th sample
L = []
print('Max len', tokenDset.maxLen())
for i, (img, target) in enumerate(cap):
print(i, ":", tokenDset.getRand(i), img.size())
if i > 10:
break
import torchvision.datasets as dset
import torchvision.transforms as transforms
cap = dset.CocoCaptions(
root='./coco/images',
annFile='./coco/annotations/captions_val2014.json',
)
#transform=transforms.ToTensor())
print('Number of samples: ', len(cap))
#img, target = cap[3] # load 4th sample
L = []
for i, (img, target) in enumerate(cap):
for s in target:
L.append("{}|{}\n".format(i, s))
print("{}|{}".format(i, s))
with open("ann_export.txt", "w", encoding='utf-8') as Fp:
Fp.writelines(L)