# -*- coding: utf-8 -*- import os import re import html import urllib.parse as ul import ftfy import torch from bs4 import BeautifulSoup from transformers import T5EncoderModel, AutoTokenizer from huggingface_hub import hf_hub_download class T5Embedder: available_models = ['t5-v1_1-xxl'] bad_punct_regex = re.compile(r'['+'#®•©™&@·º½¾¿¡§~'+'\)'+'\('+'\]'+'\['+'\}'+'\{'+'\|'+'\\'+'\/'+'\*' + r']{1,}') # noqa def __init__(self, device, dir_or_name='t5-v1_1-xxl', *, cache_dir='./cache_dir', hf_token=None, use_text_preprocessing=True, t5_model_kwargs=None, torch_dtype=None, model_max_length=120): self.device = torch.device(device) self.torch_dtype = torch_dtype or torch.bfloat16 if t5_model_kwargs is None: t5_model_kwargs = {'low_cpu_mem_usage': True, 'torch_dtype': self.torch_dtype} t5_model_kwargs['device_map'] = {'shared': self.device, 'encoder': self.device} self.use_text_preprocessing = use_text_preprocessing self.hf_token = hf_token self.cache_dir = cache_dir self.dir_or_name = dir_or_name cache_dir = os.path.join(self.cache_dir, 't5-v1_1-xxl') for filename in ['config.json', 'special_tokens_map.json', 'spiece.model', 'tokenizer_config.json', 'pytorch_model-00001-of-00002.bin', 'pytorch_model-00002-of-00002.bin', 'pytorch_model.bin.index.json']: hf_hub_download(repo_id='DeepFloyd/t5-v1_1-xxl', filename=filename, cache_dir=cache_dir, force_filename=filename, token=self.hf_token) print(cache_dir) self.tokenizer = AutoTokenizer.from_pretrained(cache_dir) self.model = T5EncoderModel.from_pretrained(cache_dir, **t5_model_kwargs).eval() self.model_max_length = model_max_length def get_text_embeddings(self, texts): texts = [self.text_preprocessing(text) for text in texts] text_tokens_and_mask = self.tokenizer( texts, max_length=self.model_max_length, padding='max_length', truncation=True, return_attention_mask=True, add_special_tokens=True, return_tensors='pt' ) text_tokens_and_mask['input_ids'] = text_tokens_and_mask['input_ids'] text_tokens_and_mask['attention_mask'] = text_tokens_and_mask['attention_mask'] with torch.no_grad(): text_encoder_embs = self.model( input_ids=text_tokens_and_mask['input_ids'].to(self.device), attention_mask=text_tokens_and_mask['attention_mask'].to(self.device), )['last_hidden_state'].detach() return text_encoder_embs, text_tokens_and_mask['attention_mask'].to(self.device) def text_preprocessing(self, text): if self.use_text_preprocessing: # The exact text cleaning as was in the training stage: text = self.clean_caption(text) text = self.clean_caption(text) return text else: return text.lower().strip() @staticmethod def basic_clean(text): text = ftfy.fix_text(text) text = html.unescape(html.unescape(text)) return text.strip() def clean_caption(self, caption): caption = str(caption) caption = ul.unquote_plus(caption) caption = caption.strip().lower() caption = re.sub('', 'person', caption) # urls: caption = re.sub( r'\b((?:https?:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))', # noqa '', caption) # regex for urls caption = re.sub( r'\b((?:www:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))', # noqa '', caption) # regex for urls # html: caption = BeautifulSoup(caption, features='html.parser').text # @ caption = re.sub(r'@[\w\d]+\b', '', caption) # 31C0—31EF CJK Strokes # 31F0—31FF Katakana Phonetic Extensions # 3200—32FF Enclosed CJK Letters and Months # 3300—33FF CJK Compatibility # 3400—4DBF CJK Unified Ideographs Extension A # 4DC0—4DFF Yijing Hexagram Symbols # 4E00—9FFF CJK Unified Ideographs caption = re.sub(r'[\u31c0-\u31ef]+', '', caption) caption = re.sub(r'[\u31f0-\u31ff]+', '', caption) caption = re.sub(r'[\u3200-\u32ff]+', '', caption) caption = re.sub(r'[\u3300-\u33ff]+', '', caption) caption = re.sub(r'[\u3400-\u4dbf]+', '', caption) caption = re.sub(r'[\u4dc0-\u4dff]+', '', caption) caption = re.sub(r'[\u4e00-\u9fff]+', '', caption) ####################################################### # все виды тире / all types of dash --> "-" caption = re.sub( r'[\u002D\u058A\u05BE\u1400\u1806\u2010-\u2015\u2E17\u2E1A\u2E3A\u2E3B\u2E40\u301C\u3030\u30A0\uFE31\uFE32\uFE58\uFE63\uFF0D]+', # noqa '-', caption) # кавычки к одному стандарту caption = re.sub(r'[`´«»“”¨]', '"', caption) caption = re.sub(r'[‘’]', "'", caption) # " caption = re.sub(r'"?', '', caption) # & caption = re.sub(r'&', '', caption) # ip adresses: caption = re.sub(r'\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}', ' ', caption) # article ids: caption = re.sub(r'\d:\d\d\s+$', '', caption) # \n caption = re.sub(r'\\n', ' ', caption) # "#123" caption = re.sub(r'#\d{1,3}\b', '', caption) # "#12345.." caption = re.sub(r'#\d{5,}\b', '', caption) # "123456.." caption = re.sub(r'\b\d{6,}\b', '', caption) # filenames: caption = re.sub(r'[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)', '', caption) # caption = re.sub(r'[\"\']{2,}', r'"', caption) # """AUSVERKAUFT""" caption = re.sub(r'[\.]{2,}', r' ', caption) # """AUSVERKAUFT""" caption = re.sub(self.bad_punct_regex, r' ', caption) # ***AUSVERKAUFT***, #AUSVERKAUFT caption = re.sub(r'\s+\.\s+', r' ', caption) # " . " # this-is-my-cute-cat / this_is_my_cute_cat regex2 = re.compile(r'(?:\-|\_)') if len(re.findall(regex2, caption)) > 3: caption = re.sub(regex2, ' ', caption) caption = self.basic_clean(caption) caption = re.sub(r'\b[a-zA-Z]{1,3}\d{3,15}\b', '', caption) # jc6640 caption = re.sub(r'\b[a-zA-Z]+\d+[a-zA-Z]+\b', '', caption) # jc6640vc caption = re.sub(r'\b\d+[a-zA-Z]+\d+\b', '', caption) # 6640vc231 caption = re.sub(r'(worldwide\s+)?(free\s+)?shipping', '', caption) caption = re.sub(r'(free\s)?download(\sfree)?', '', caption) caption = re.sub(r'\bclick\b\s(?:for|on)\s\w+', '', caption) caption = re.sub(r'\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\simage[s]?)?', '', caption) caption = re.sub(r'\bpage\s+\d+\b', '', caption) caption = re.sub(r'\b\d*[a-zA-Z]+\d+[a-zA-Z]+\d+[a-zA-Z\d]*\b', r' ', caption) # j2d1a2a... caption = re.sub(r'\b\d+\.?\d*[xх×]\d+\.?\d*\b', '', caption) caption = re.sub(r'\b\s+\:\s+', r': ', caption) caption = re.sub(r'(\D[,\./])\b', r'\1 ', caption) caption = re.sub(r'\s+', ' ', caption) caption.strip() caption = re.sub(r'^[\"\']([\w\W]+)[\"\']$', r'\1', caption) caption = re.sub(r'^[\'\_,\-\:;]', r'', caption) caption = re.sub(r'[\'\_,\-\:\-\+]$', r'', caption) caption = re.sub(r'^\.\S+$', '', caption) return caption.strip() if __name__ == '__main__': t5 = T5Embedder(device="cuda", cache_dir='./cache_dir', torch_dtype=torch.float) device = t5.device prompts = ['I am a test caption', 'Test twice'] with torch.no_grad(): caption_embs, emb_masks = t5.get_text_embeddings(prompts) emb_dict = { 'caption_feature': caption_embs.float().cpu().data.numpy(), 'attention_mask': emb_masks.cpu().data.numpy(), } import ipdb;ipdb.set_trace() print()