Spaces:
Runtime error
Runtime error
File size: 1,674 Bytes
c7f5de3 |
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 |
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
|