File size: 8,438 Bytes
b3f324b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 |
# -*- coding: utf-8 -*-
import os
import re
import ftfy
import torch
import html
from PIL import Image
from transformers import CLIPProcessor, CLIPModel, CLIPTokenizer, CLIPTextModel
class CLIPEmbedder:
"""
A class for embedding texts and images using a pretrained CLIP model.
"""
def __init__(self, device='cuda', model_name='openai/clip-vit-base-patch32', cache_dir='./cache_dir', use_text_preprocessing=True, max_length=77):
"""
Initializes the CLIPEmbedder with specified model and configurations.
"""
self.device = torch.device(device)
self.model_name = model_name
self.cache_dir = cache_dir
self.use_text_preprocessing = use_text_preprocessing
self.max_length = max_length
os.makedirs(self.cache_dir, exist_ok=True)
self.processor = CLIPProcessor.from_pretrained(model_name, cache_dir=self.cache_dir)
self.model = CLIPModel.from_pretrained(model_name, cache_dir=self.cache_dir).to(self.device).eval()
self.tokenizer = CLIPTokenizer.from_pretrained(model_name)
self.text_model = CLIPTextModel.from_pretrained(model_name, cache_dir=self.cache_dir).to(self.device).eval()
for param in self.text_model.parameters():
param.requires_grad = False
def get_text_embeddings(self, texts):
"""
Generates embeddings for a list of text prompts.
"""
self._validate_input_list(texts, str)
if self.use_text_preprocessing:
texts = [self._clean_text(text) for text in texts]
inputs = self.processor(text=texts, return_tensors="pt", padding=True, truncation=True, max_length=self.max_length).to(self.device)
with torch.no_grad():
embeddings = self.model.get_text_features(**inputs)
return embeddings
def encode_text(self, texts):
"""
Encodes texts into embeddings and returns the last hidden state and pooled output.
"""
self._validate_input_list(texts, str)
batch_encoding = self.tokenizer(texts, return_tensors="pt", truncation=True, max_length=self.max_length, padding="max_length").to(self.device)
with torch.no_grad():
outputs = self.text_model(**batch_encoding)
return outputs.last_hidden_state, outputs.pooler_output
def get_image_embeddings(self, image_paths):
"""
Generates embeddings for a list of image file paths.
"""
self._validate_input_list(image_paths, str)
images = [self._load_image(path) for path in image_paths]
inputs = self.processor(images=images, return_tensors="pt").to(self.device)
with torch.no_grad():
embeddings = self.model.get_image_features(**inputs)
return embeddings
def _validate_input_list(self, input_list, expected_type):
"""
Validates that the input is a list of expected type.
"""
if not isinstance(input_list, list) or not all(isinstance(item, expected_type) for item in input_list):
raise ValueError(f"Input must be a list of {expected_type.__name__}.")
def _clean_text(self, text):
"""
Applies basic cleaning and formatting to a text string.
"""
text = ftfy.fix_text(text)
text = html.unescape(text)
return text.strip()
def _load_image(self, image_path):
"""
Loads and preprocesses an image from a file path.
"""
try:
image = Image.open(image_path).convert("RGB")
except FileNotFoundError:
raise FileNotFoundError(f"Image file not found: {image_path}")
except Exception as e:
raise Exception(f"Error loading image {image_path}: {e}")
return image
def clean_caption(self, caption):
caption = str(caption)
caption = ul.unquote_plus(caption)
caption = caption.strip().lower()
caption = re.sub('<person>', '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
caption = BeautifulSoup(caption, features='html.parser').text
caption = re.sub(r'@[\w\d]+\b', '', caption)
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)
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)
caption = re.sub(r'[`´«»“”¨]', '"', caption)
caption = re.sub(r'[‘’]', "'", caption)
caption = re.sub(r'"?', '', caption)
caption = re.sub(r'&', '', caption)
caption = re.sub(r'\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}', ' ', caption)
caption = re.sub(r'\d:\d\d\s+$', '', caption)
caption = re.sub(r'\\n', ' ', caption)
caption = re.sub(r'#\d{1,3}\b', '', caption)
caption = re.sub(r'#\d{5,}\b', '', caption)
caption = re.sub(r'\b\d{6,}\b', '', caption)
caption = re.sub(r'[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)', '', caption)
caption = re.sub(r'[\"\']{2,}', r'"', caption)
caption = re.sub(r'[\.]{2,}', r' ', caption)
caption = re.sub(self.bad_punct_regex, r' ', caption)
caption = re.sub(r'\s+\.\s+', r' ', caption)
regex2 = re.compile(r'(?:\-|\_)')
if len(re.findall(regex2, caption)) > 3:
caption = re.sub(regex2, ' ', caption)
caption = self.basic_clean(caption)
caption = re.sub(r'\b[a-zA-Z]{1,3}\d{3,15}\b', '', caption) # jc6640
caption = re.sub(r'\b[a-zA-Z]+\d+[a-zA-Z]+\b', '', caption) # jc6640vc
caption = re.sub(r'\b\d+[a-zA-Z]+\d+\b', '', caption) # 6640vc231
caption = re.sub(r'(worldwide\s+)?(free\s+)?shipping', '', caption)
caption = re.sub(r'(free\s)?download(\sfree)?', '', caption)
caption = re.sub(r'\bclick\b\s(?:for|on)\s\w+', '', caption)
caption = re.sub(r'\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\simage[s]?)?', '', caption)
caption = re.sub(r'\bpage\s+\d+\b', '', caption)
caption = re.sub(r'\b\d*[a-zA-Z]+\d+[a-zA-Z]+\d+[a-zA-Z\d]*\b', r' ', caption) # j2d1a2a...
caption = re.sub(r'\b\d+\.?\d*[xх×]\d+\.?\d*\b', '', caption)
caption = re.sub(r'\b\s+\:\s+', r': ', caption)
caption = re.sub(r'(\D[,\./])\b', r'\1 ', caption)
caption = re.sub(r'\s+', ' ', caption)
caption.strip()
caption = re.sub(r'^[\"\']([\w\W]+)[\"\']$', r'\1', caption)
caption = re.sub(r'^[\'\_,\-\:;]', r'', caption)
caption = re.sub(r'[\'\_,\-\:\-\+]$', r'', caption)
caption = re.sub(r'^\.\S+$', '', caption)
return caption.strip()
@staticmethod
def basic_clean(text):
text = ftfy.fix_text(text)
text = html.unescape(html.unescape(text))
return text.strip()
if __name__ == '__main__':
clip_embedder = CLIPEmbedder()
# Example
text_prompts = [
'A photo of a cute puppy playing with a ball.',
'An image of a beautiful sunset over the ocean.',
'A scene depicting a busy city street.'
]
text_embeddings = clip_embedder.get_text_embeddings(text_prompts)
print(f"Text embeddings shape: {text_embeddings.shape}")
image_paths = ['image1.jpg', 'image2.png']
try:
image_embeddings = clip_embedder.get_image_embeddings(image_paths)
print(f"Image embeddings shape: {image_embeddings.shape}")
except FileNotFoundError as e:
print(e)
except Exception as e:
print(f"An error occurred: {e}")
|