| from transformers import VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer | |
| import torch | |
| from PIL import Image | |
| from PIL import Image | |
| from transformers import AutoProcessor, BlipForQuestionAnswering | |
| import torch | |
| from models import load_transformers | |
| class vit_gpt2: | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| max_length = 16 | |
| num_beams = 4 | |
| gen_kwargs = {"max_length": max_length, "num_beams": num_beams} | |
| def __init__(self, model_pretrain:str = "nlpconnect/vit-gpt2-image-captioning"): | |
| self.model = VisionEncoderDecoderModel.from_pretrained(model_pretrain | |
| , device_map={"": 0}, torch_dtype=torch.float16) | |
| self.feature_extractor = ViTImageProcessor.from_pretrained(model_pretrain) | |
| self.tokenizer = AutoTokenizer.from_pretrained(model_pretrain) | |
| def image_captioning(self, image: Image.Image) -> str: | |
| pixel_values = self.feature_extractor(images=[image], return_tensors="pt").pixel_values | |
| pixel_values = pixel_values.to(self.device) | |
| output_ids = self.model.generate(pixel_values, **self.gen_kwargs) | |
| preds = self.tokenizer.batch_decode(output_ids, skip_special_tokens=True) | |
| return preds[0] | |
| def visual_question_answering(self, image: Image.Image, prompt: str) -> str: | |
| inputs = self.processor(images=image, text=prompt, return_tensors="pt").to(self.device, torch.float16) | |
| generated_ids = self.model.generate(**inputs) | |
| generated_text = self.processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip() | |
| return generated_text | |