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from PIL import Image
import requests
from transformers import Blip2Processor, Blip2ForConditionalGeneration
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
print(device)
def init_BLIP(device):
processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
if device == 'cuda':
model = Blip2ForConditionalGeneration.from_pretrained(
"Salesforce/blip2-opt-2.7b", load_in_8bit=True,torch_dtype=torch.float16, device_map = 'auto')
else:
print('Using CPU model')
model = Blip2ForConditionalGeneration.from_pretrained( "Salesforce/blip2-opt-2.7b",device_map={"": device}, torch_dtype=torch.float32,low_cpu_mem_usage=True)
model.eval()
if torch.__version__ >= "2":
model = torch.compile(model)
processor = processor
return model,processor
def infer_BLIP2(model,processor,image,device):
outputs= ''
prompts = [
"This is a picture of",
"Question: What is in the picture? Answer:",
"Question: Where is this image depicting? Answer:",
"Question: Who is in this picture? Answer:",
"Question: What are the things in the picture doing? Answer:",
"Question: Why do you think they are doing it? Answer:",
"Question: What emotion does the person or animal in the image feel? Answer:",
]
for prompt in prompts:
if device == 'cuda':
inputs = processor(images=image, text=prompt, return_tensors="pt").to(device, torch.float16)
else:
inputs = processor(images=image, text=prompt, return_tensors="pt")
generated_ids = model.generate(**inputs)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
outputs+= prompt+generated_text+' '
return outputs
'''
Testing
model,processor = init_BLIP(device)
image = Image.open('/home/spooky/Downloads/IMG20221214012021.jpg')
infer_BLIP2(model,processor,image,device)'''
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