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4e5482b 84ec70a 4e5482b | 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 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | import argparse
from pathlib import Path
import sys
from PIL import Image
import torch
from transformers import AutoImageProcessor, AutoModel
sys.path.insert(0, str(Path(__file__).absolute().parents[1]))
from projector import load_projector
MODEL_ID = "immanuelpeter/MoonViT-K2.6"
def main() -> None:
parser = argparse.ArgumentParser(description="Extract Kimi K2.6 projected 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
tower = AutoModel.from_pretrained(
args.model,
dtype=dtype,
trust_remote_code=True,
).to(device).eval()
processor = AutoImageProcessor.from_pretrained(args.model, trust_remote_code=True)
projector = load_projector(args.model)
projector.to(device=device, dtype=dtype).eval()
inputs = processor(images=Image.open(args.image).convert("RGB"), return_tensors="pt")
with torch.inference_mode():
merged = tower(
inputs["pixel_values"].to(device=device, dtype=dtype),
inputs["grid_thws"].to(device),
)
projected = projector(merged)
print("merged", torch.stack(merged).flatten(2).shape)
print("projected", torch.stack(projected).shape)
if __name__ == "__main__":
main()
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