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204c1fe 0f0835a 204c1fe 0f0835a 204c1fe 0f0835a 204c1fe 3cd67e8 204c1fe 0f0835a 204c1fe 0f0835a 204c1fe | 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 48 49 50 51 52 53 54 | 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, merged_and_projected
MODEL_ID = "immanuelpeter/PixtralViT-Large-4"
def square(image: Image.Image, side: int) -> Image.Image:
edge = min(image.size)
left, top = (image.width - edge) // 2, (image.height - edge) // 2
return image.crop((left, top, left + edge, top + edge)).resize((side, side), Image.BICUBIC)
def main() -> None:
parser = argparse.ArgumentParser(description="Extract Mistral Large 4 visual features.")
parser.add_argument("image", type=Path)
parser.add_argument("--model", default=MODEL_ID)
parser.add_argument("--size", type=int, default=448, help="square side in pixels, a multiple of 28")
args = parser.parse_args()
if args.size % 28:
raise SystemExit("--size must be a multiple of 28")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
dtype = torch.bfloat16 if device.type == "cuda" else torch.float32
# trust_remote_code runs modeling_pixtral_large4.py, the parent's BF16 numeric order.
tower = AutoModel.from_pretrained(args.model, dtype=dtype, trust_remote_code=True).to(device).eval()
processor = AutoImageProcessor.from_pretrained(args.model)
projector = load_projector(args.model).to(device=device, dtype=dtype).eval()
image = square(Image.open(args.image).convert("RGB"), args.size)
inputs = processor(images=image, size={"longest_edge": args.size}, return_tensors="pt")
image_sizes = inputs["image_sizes"].tolist()
with torch.inference_mode():
pixels = inputs["pixel_values"].to(device, dtype)
tower_tokens = tower(pixels, image_sizes=image_sizes).last_hidden_state.squeeze(0)
merged, projected = merged_and_projected(projector, tower_tokens, image_sizes)
print("device", device, "dtype", dtype, "image_sizes", image_sizes)
print("tower", tuple(tower_tokens.shape))
print("merged", tuple(merged.shape))
print("projected", tuple(projected.shape))
if __name__ == "__main__":
main()
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