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bdfd5fd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | import argparse
from pathlib import Path
from PIL import Image
import torch
from transformers import AutoImageProcessor, AutoModel
MODEL_ID = "immanuelpeter/MiMoViT-2.6-Pro"
def main() -> None:
parser = argparse.ArgumentParser(description="Extract MiMo 2.6 Pro visual features.")
parser.add_argument("image", type=Path)
parser.add_argument("--model", default=MODEL_ID)
args = parser.parse_args()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
dtype = torch.bfloat16 if device.type == "cuda" else torch.float32
processor = AutoImageProcessor.from_pretrained(args.model)
model = AutoModel.from_pretrained(args.model, trust_remote_code=True, dtype=dtype).to(device).eval()
inputs = processor(images=Image.open(args.image).convert("RGB"), return_tensors="pt")
with torch.inference_mode():
projected = model(inputs["pixel_values"].to(device, dtype), inputs["image_grid_thw"])
print("grid_thw", inputs["image_grid_thw"].tolist())
print("projected", tuple(projected.shape))
if __name__ == "__main__":
main()
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