from PIL import Image import requests from transformers import Blip2Processor, Blip2ForConditionalGeneration, BlipProcessor, BlipForConditionalGeneration import torch from utils.util import resize_long_edge class ImageCaptioning: def __init__(self, device, captioner_base_model='blip'): self.device = device self.captioner_base_model = captioner_base_model self.processor, self.model = self.initialize_model() def initialize_model(self,): if self.device == 'cpu': self.data_type = torch.float32 else: self.data_type = torch.float16 if self.captioner_base_model == 'blip2': processor = Blip2Processor.from_pretrained("pretrained_models/blip2-opt-2.7b") model = Blip2ForConditionalGeneration.from_pretrained( "pretrained_models/blip2-opt-2.7b", torch_dtype=self.data_type ) # for gpu with small memory elif self.captioner_base_model == 'blip': processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base") model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base", torch_dtype=self.data_type) else: raise ValueError('arch not supported') model.to(self.device) return processor, model def image_caption(self, image_src): image = Image.open(image_src) image = resize_long_edge(image, 384) inputs = self.processor(images=image, return_tensors="pt").to(self.device, self.data_type) generated_ids = self.model.generate(**inputs) generated_text = self.processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip() print('\033[1;35m' + '*' * 100 + '\033[0m') print('\nStep1, BLIP2 caption:') print(generated_text) print('\033[1;35m' + '*' * 100 + '\033[0m') return generated_text def image_caption_debug(self, image_src): return "A dish with salmon, broccoli, and something yellow."