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from PIL import Image
import requests
from transformers import Blip2Processor, Blip2ForConditionalGeneration
import torch
class ImageCaptioning:
def __init__(self) -> None:
self.device = None
# self.processor, self.model = None, None
self.processor, self.model = self.initialize_model()
def initialize_model(self):
# device = "cuda" if torch.cuda.is_available() else "cpu"
self.device = "cpu" # for low gpu memory devices
if self.device == 'cpu':
self.data_type = torch.float32
else:
self.data_type = torch.float16
processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
model = Blip2ForConditionalGeneration.from_pretrained(
"Salesforce/blip2-opt-2.7b", torch_dtype=self.data_type
)
model.to(self.device)
return processor, model
def image_caption(self, image_src):
image = Image.open(image_src)
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('*'*100 + '\nStep1, BLIP2 caption:')
print(generated_text)
print('\n' + '*'*100)
return generated_text
def image_caption_debug(self, image_src):
return "A dish with salmon, broccoli, and something yellow." |