run_test.py

"""Smoke-test a packaged local or downloaded Mettle-MX repository."""

import argparse

import torch
from PIL import Image
from transformers import AutoImageProcessor, AutoModel


def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument("model", help="local directory or Hugging Face repository ID")
    parser.add_argument("--image", help="optional RGB image to encode")
    parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu")
    return parser.parse_args()


def main():
    args = parse_args()
    processor = AutoImageProcessor.from_pretrained(args.model)
    model = AutoModel.from_pretrained(
        args.model,
        trust_remote_code=True,
    ).to(args.device).eval()
    if args.image:
        image = Image.open(args.image).convert("RGB")
    else:
        image = Image.new("RGB", (224, 224), color=(128, 96, 128))
    inputs = processor(images=image, return_tensors="pt")
    pixel_values = inputs["pixel_values"].to(args.device)
    with torch.inference_mode():
        cls = model.encode(pixel_values, feature_view="cls")
        cls_mean = model.encode(pixel_values, feature_view="cls_mean")
    if tuple(cls.shape) != (1, 1536):
        raise RuntimeError(f"unexpected CLS shape: {tuple(cls.shape)}")
    if tuple(cls_mean.shape) != (1, 3072):
        raise RuntimeError(f"unexpected CLS+mean shape: {tuple(cls_mean.shape)}")
    if not torch.equal(cls, cls_mean[:, :1536]):
        raise RuntimeError("CLS differs between the two feature views")
    print(f"PASS cls={tuple(cls.shape)} cls_mean={tuple(cls_mean.shape)}")


if __name__ == "__main__":
    main()