| import os | |
| import torch | |
| import PIL | |
| from PIL import Image | |
| from transformers import InstructBlipProcessor, InstructBlipForConditionalGeneration | |
| import bitsandbytes | |
| import accelerate | |
| import captioning_config as config | |
| class ImageCaptioningModel: | |
| def __init__(self): | |
| self.model_type = config.MODEL_TYPE | |
| self.processor = None | |
| self.model = None | |
| self.prompt = config.PROMPT | |
| self.max_image_size = config.MAX_IMAGE_SIZE | |
| self.min_length = config.MIN_LENGTH | |
| self.max_new_tokens = config.MAX_NEW_TOKENS | |
| self.model_path = config.MODEL_PATH | |
| self.device_map = config.DEVICE_MAP | |
| self.torch_dtype = config.TORCH_DTYPE | |
| self.load_in_8bit = config.LOAD_IN_8BIT | |
| self.low_cpu_mem_usage = config.LOW_CPU_MEM_USAGE | |
| self.skip_secial_tokens = config.SKIP_SPECIAL_TOKENS | |
| def load_model(self): | |
| if self.model_type == 'i_blip': | |
| self.processor = InstructBlipProcessor.from_pretrained(self.model_path, | |
| load_in_8bit=self.load_in_8bit, | |
| torch_dtype=self.torch_dtype, | |
| device_map=self.device_map | |
| ) | |
| self.model = InstructBlipForConditionalGeneration.from_pretrained(self.model_path, | |
| load_in_8bit=self.load_in_8bit, | |
| torch_dtype=self.torch_dtype, | |
| low_cpu_mem_usage=self.low_cpu_mem_usage, | |
| device_map=self.device_map | |
| ) | |
| def resize_image(self, image, max_image_size=None): | |
| if max_image_size is None: | |
| max_image_size = int(os.getenv("MAX_IMAGE_SIZE", "1024")) | |
| h, w = image.size | |
| scale = max_image_size / max(h, w) | |
| if scale < 1: | |
| new_w = int(w * scale) | |
| new_h = int(h * scale) | |
| image = image.resize((new_w, new_h), resample=PIL.Image.Resampling.LANCZOS) | |
| return image | |
| def generate_caption(self, image_path): | |
| image = Image.open(image_path) | |
| image = self.resize_image(image) | |
| inputs = self.processor(image, self.prompt, return_tensors="pt").to("cuda", self.torch_dtype) | |
| outputs = self.model.generate(**inputs, min_length=self.min_length, max_new_tokens=self.max_new_tokens) | |
| caption = self.processor.decode(outputs[0], skip_special_tokens=self.skip_secial_tokens).strip() | |
| return caption | |
| def generate_captions_for_multiple_images(self, image_paths): | |
| return [self.generate_caption(image_path) for image_path in image_paths] | |
| if __name__ == "__main__": | |
| pass | |