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import os |
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import re |
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import ftfy |
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import torch |
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import html |
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from PIL import Image |
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from transformers import CLIPProcessor, CLIPModel, CLIPTokenizer, CLIPTextModel |
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class CLIPEmbedder: |
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""" |
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A class for embedding texts and images using a pretrained CLIP model. |
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""" |
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def __init__(self, device='cuda', model_name='openai/clip-vit-base-patch32', cache_dir='./cache_dir', use_text_preprocessing=True, max_length=77): |
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""" |
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Initializes the CLIPEmbedder with specified model and configurations. |
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""" |
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self.device = torch.device(device) |
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self.model_name = model_name |
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self.cache_dir = cache_dir |
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self.use_text_preprocessing = use_text_preprocessing |
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self.max_length = max_length |
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os.makedirs(self.cache_dir, exist_ok=True) |
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self.processor = CLIPProcessor.from_pretrained(model_name, cache_dir=self.cache_dir) |
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self.model = CLIPModel.from_pretrained(model_name, cache_dir=self.cache_dir).to(self.device).eval() |
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self.tokenizer = CLIPTokenizer.from_pretrained(model_name) |
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self.text_model = CLIPTextModel.from_pretrained(model_name, cache_dir=self.cache_dir).to(self.device).eval() |
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for param in self.text_model.parameters(): |
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param.requires_grad = False |
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def get_text_embeddings(self, texts): |
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""" |
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Generates embeddings for a list of text prompts. |
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""" |
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self._validate_input_list(texts, str) |
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if self.use_text_preprocessing: |
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texts = [self._clean_text(text) for text in texts] |
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inputs = self.processor(text=texts, return_tensors="pt", padding=True, truncation=True, max_length=self.max_length).to(self.device) |
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with torch.no_grad(): |
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embeddings = self.model.get_text_features(**inputs) |
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return embeddings |
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def encode_text(self, texts): |
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""" |
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Encodes texts into embeddings and returns the last hidden state and pooled output. |
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""" |
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self._validate_input_list(texts, str) |
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batch_encoding = self.tokenizer(texts, return_tensors="pt", truncation=True, max_length=self.max_length, padding="max_length").to(self.device) |
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with torch.no_grad(): |
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outputs = self.text_model(**batch_encoding) |
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return outputs.last_hidden_state, outputs.pooler_output |
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def get_image_embeddings(self, image_paths): |
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""" |
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Generates embeddings for a list of image file paths. |
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""" |
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self._validate_input_list(image_paths, str) |
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images = [self._load_image(path) for path in image_paths] |
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inputs = self.processor(images=images, return_tensors="pt").to(self.device) |
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with torch.no_grad(): |
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embeddings = self.model.get_image_features(**inputs) |
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return embeddings |
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def _validate_input_list(self, input_list, expected_type): |
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""" |
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Validates that the input is a list of expected type. |
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""" |
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if not isinstance(input_list, list) or not all(isinstance(item, expected_type) for item in input_list): |
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raise ValueError(f"Input must be a list of {expected_type.__name__}.") |
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def _clean_text(self, text): |
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""" |
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Applies basic cleaning and formatting to a text string. |
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""" |
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text = ftfy.fix_text(text) |
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text = html.unescape(text) |
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return text.strip() |
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def _load_image(self, image_path): |
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""" |
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Loads and preprocesses an image from a file path. |
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""" |
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try: |
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image = Image.open(image_path).convert("RGB") |
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except FileNotFoundError: |
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raise FileNotFoundError(f"Image file not found: {image_path}") |
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except Exception as e: |
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raise Exception(f"Error loading image {image_path}: {e}") |
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return image |
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def clean_caption(self, caption): |
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caption = str(caption) |
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caption = ul.unquote_plus(caption) |
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caption = caption.strip().lower() |
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caption = re.sub('<person>', 'person', caption) |
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caption = re.sub( |
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r'\b((?:https?:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))', |
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'', caption) |
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caption = re.sub( |
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r'\b((?:www:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))', |
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'', caption) |
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caption = BeautifulSoup(caption, features='html.parser').text |
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caption = re.sub(r'@[\w\d]+\b', '', caption) |
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caption = re.sub(r'[\u31c0-\u31ef]+', '', caption) |
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caption = re.sub(r'[\u31f0-\u31ff]+', '', caption) |
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caption = re.sub(r'[\u3200-\u32ff]+', '', caption) |
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caption = re.sub(r'[\u3300-\u33ff]+', '', caption) |
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caption = re.sub(r'[\u3400-\u4dbf]+', '', caption) |
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caption = re.sub(r'[\u4dc0-\u4dff]+', '', caption) |
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caption = re.sub(r'[\u4e00-\u9fff]+', '', caption) |
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caption = re.sub( |
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r'[\u002D\u058A\u05BE\u1400\u1806\u2010-\u2015\u2E17\u2E1A\u2E3A\u2E3B\u2E40\u301C\u3030\u30A0\uFE31\uFE32\uFE58\uFE63\uFF0D]+', |
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'-', caption) |
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caption = re.sub(r'[`´«»“”¨]', '"', caption) |
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caption = re.sub(r'[‘’]', "'", caption) |
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caption = re.sub(r'"?', '', caption) |
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caption = re.sub(r'&', '', caption) |
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caption = re.sub(r'\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}', ' ', caption) |
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caption = re.sub(r'\d:\d\d\s+$', '', caption) |
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caption = re.sub(r'\\n', ' ', caption) |
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caption = re.sub(r'#\d{1,3}\b', '', caption) |
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caption = re.sub(r'#\d{5,}\b', '', caption) |
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caption = re.sub(r'\b\d{6,}\b', '', caption) |
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caption = re.sub(r'[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)', '', caption) |
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caption = re.sub(r'[\"\']{2,}', r'"', caption) |
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caption = re.sub(r'[\.]{2,}', r' ', caption) |
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caption = re.sub(self.bad_punct_regex, r' ', caption) |
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caption = re.sub(r'\s+\.\s+', r' ', caption) |
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regex2 = re.compile(r'(?:\-|\_)') |
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if len(re.findall(regex2, caption)) > 3: |
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caption = re.sub(regex2, ' ', caption) |
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caption = self.basic_clean(caption) |
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caption = re.sub(r'\b[a-zA-Z]{1,3}\d{3,15}\b', '', caption) |
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caption = re.sub(r'\b[a-zA-Z]+\d+[a-zA-Z]+\b', '', caption) |
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caption = re.sub(r'\b\d+[a-zA-Z]+\d+\b', '', caption) |
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caption = re.sub(r'(worldwide\s+)?(free\s+)?shipping', '', caption) |
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caption = re.sub(r'(free\s)?download(\sfree)?', '', caption) |
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caption = re.sub(r'\bclick\b\s(?:for|on)\s\w+', '', caption) |
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caption = re.sub(r'\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\simage[s]?)?', '', caption) |
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caption = re.sub(r'\bpage\s+\d+\b', '', caption) |
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caption = re.sub(r'\b\d*[a-zA-Z]+\d+[a-zA-Z]+\d+[a-zA-Z\d]*\b', r' ', caption) |
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caption = re.sub(r'\b\d+\.?\d*[xх×]\d+\.?\d*\b', '', caption) |
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caption = re.sub(r'\b\s+\:\s+', r': ', caption) |
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caption = re.sub(r'(\D[,\./])\b', r'\1 ', caption) |
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caption = re.sub(r'\s+', ' ', caption) |
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caption.strip() |
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caption = re.sub(r'^[\"\']([\w\W]+)[\"\']$', r'\1', caption) |
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caption = re.sub(r'^[\'\_,\-\:;]', r'', caption) |
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caption = re.sub(r'[\'\_,\-\:\-\+]$', r'', caption) |
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caption = re.sub(r'^\.\S+$', '', caption) |
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return caption.strip() |
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@staticmethod |
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def basic_clean(text): |
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text = ftfy.fix_text(text) |
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text = html.unescape(html.unescape(text)) |
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return text.strip() |
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if __name__ == '__main__': |
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clip_embedder = CLIPEmbedder() |
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text_prompts = [ |
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'A photo of a cute puppy playing with a ball.', |
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'An image of a beautiful sunset over the ocean.', |
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'A scene depicting a busy city street.' |
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] |
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text_embeddings = clip_embedder.get_text_embeddings(text_prompts) |
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print(f"Text embeddings shape: {text_embeddings.shape}") |
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image_paths = ['image1.jpg', 'image2.png'] |
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try: |
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image_embeddings = clip_embedder.get_image_embeddings(image_paths) |
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print(f"Image embeddings shape: {image_embeddings.shape}") |
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except FileNotFoundError as e: |
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print(e) |
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except Exception as e: |
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print(f"An error occurred: {e}") |
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