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import torch
from PIL import Image
from transformers import Blip2Processor, Blip2ForConditionalGeneration, BlipProcessor, BlipForConditionalGeneration
class ImageCaptioner:
def __init__(self, model_name="blip2-opt", device="cpu"):
self.model_name = model_name
self.device = device
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
processor, model = None, None
if self.model_name == "blip2-opt":
processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b-coco")
model = Blip2ForConditionalGeneration.from_pretrained(
"Salesforce/blip2-opt-2.7b-coco", torch_dtype=self.data_type, low_cpu_mem_usage=True)
elif self.model_name == "blip2-flan-t5":
processor = Blip2Processor.from_pretrained("Salesforce/blip2-flan-t5-xl")
model = Blip2ForConditionalGeneration.from_pretrained(
"Salesforce/blip2-flan-t5-xl", torch_dtype=self.data_type, low_cpu_mem_usage=True)
# for gpu with small memory
elif self.model_name == "blip":
processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
else:
raise NotImplementedError(f"{self.model_name} not implemented.")
model.to(self.device)
if self.device != 'cpu':
model.half()
return processor, model
def image_caption(self, image):
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()
return generated_text
def image_caption_debug(self, image_src):
return "A dish with salmon, broccoli, and something yellow."
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