detect_demo_pvalue.py

from transformers import AutoModelForImageClassification, BlipImageProcessor
from huggingface_hub import hf_hub_download
from safetensors import safe_open

from PIL import Image
import sys
import torch

image_processor = BlipImageProcessor.from_pretrained("imatag/stable-signature-bzh-detector-resnet18")
model = AutoModelForImageClassification.from_pretrained("imatag/stable-signature-bzh-detector-resnet18")
calibration = hf_hub_download("imatag/stable-signature-bzh-detector-resnet18", filename="calibration.safetensors")
with safe_open(calibration, framework="pt") as f:
    calibration_logits = f.get_tensor("logits")

filename = sys.argv[1]
img = Image.open(filename).convert("RGB")
inputs = image_processor(img, return_tensors="pt")
with torch.no_grad():
    p = model(**inputs).logits[...,0:1]
    p = (1 + torch.sum(calibration_logits <= p, dim=-1)) / calibration_logits.shape[0]
    p = p.item()

print(f"approximate p-value: {p}")