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import torch
from PIL import Image
from torchvision import transforms
from model import load_model
# Preprocessing
_transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.4815, 0.4578, 0.4082],
std=[0.2686, 0.2613, 0.2758]
)
])
def load_for_inference(repo_id, filename="model.pt"):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = load_model(repo_id=repo_id, filename=filename, device=device)
tokenizer = model.tokenizer
return model, tokenizer, device
def predict(model, tokenizer, device, image: Image.Image, question: str):
image_tensor = _transform(image).unsqueeze(0).to(device)
q = tokenizer(
question,
return_tensors='pt',
padding=True,
truncation=True,
max_length=64
).to(device)
with torch.no_grad():
output_ids = model.generate(
images=image_tensor,
input_ids=q.input_ids,
attention_mask=q.attention_mask,
max_length=64,
num_beams=4
)
return tokenizer.decode(output_ids[0], skip_special_tokens=True)
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