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from PIL import Image
import requests
from transformers import Blip2Processor, Blip2ForConditionalGeneration, BlipProcessor, BlipForConditionalGeneration
import torch
from utils.util import resize_long_edge
class ImageCaptioning:
def __init__(self, device, captioner_base_model='blip'):
self.device = device
self.captioner_base_model = captioner_base_model
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
if self.captioner_base_model == 'blip2':
processor = Blip2Processor.from_pretrained("pretrained_models/blip2-opt-2.7b")
model = Blip2ForConditionalGeneration.from_pretrained(
"pretrained_models/blip2-opt-2.7b", torch_dtype=self.data_type
)
# for gpu with small memory
elif self.captioner_base_model == 'blip':
processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base", torch_dtype=self.data_type)
else:
raise ValueError('arch not supported')
model.to(self.device)
return processor, model
def image_caption(self, image_src):
image = Image.open(image_src)
image = resize_long_edge(image, 384)
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('\033[1;35m' + '*' * 100 + '\033[0m')
print('\nStep1, BLIP2 caption:')
print(generated_text)
print('\033[1;35m' + '*' * 100 + '\033[0m')
return generated_text
def image_caption_debug(self, image_src):
return "A dish with salmon, broccoli, and something yellow." |