| """Encode an image, or run a synthetic smoke check when no image is supplied.""" |
|
|
| import argparse |
| from pathlib import Path |
|
|
| import coremltools as ct |
| import numpy as np |
| from PIL import Image, ImageOps |
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|
|
| def main() -> None: |
| parser = argparse.ArgumentParser(description=__doc__) |
| parser.add_argument("image", type=Path, nargs="?") |
| parser.add_argument("--model", type=Path, default=Path("models/DINOv3ViTB16-FP32-448.mlpackage")) |
| parser.add_argument("--output", type=Path) |
| args = parser.parse_args() |
| model = ct.models.MLModel(str(args.model), compute_units=ct.ComputeUnit.ALL) |
| image_type = model.get_spec().description.input[0].type.imageType |
| size = (image_type.width, image_type.height) |
| if args.image: |
| with Image.open(args.image) as source: |
| image = ImageOps.exif_transpose(source).convert("RGB").resize(size, Image.Resampling.BICUBIC) |
| else: |
| y, x = np.mgrid[0:size[1], 0:size[0]] |
| pixels = np.stack((x * 255 // size[0], y * 255 // size[1], (x + y) * 255 // sum(size)), axis=-1).astype(np.uint8) |
| image = Image.fromarray(pixels) |
| prediction = model.predict({"image": image}) |
| vector = np.asarray(prediction["embedding"], dtype=np.float32).reshape(-1) |
| patches = np.asarray(prediction["patch_embeddings"], dtype=np.float32).reshape(-1, 768) |
| if vector.shape != (768,) or not np.isfinite(vector).all(): |
| raise SystemExit("Invalid embedding") |
| if not np.isfinite(patches).all(): |
| raise SystemExit("Invalid patch embeddings") |
| norm = float(np.linalg.norm(vector)) |
| if not np.isclose(norm, 1, atol=1e-4): |
| raise SystemExit(f"Embedding is not normalized: {norm}") |
| if args.output: |
| args.output.parent.mkdir(parents=True, exist_ok=True) |
| np.save(args.output, vector) |
| print(f"shape={vector.shape}, dtype={vector.dtype}, L2 norm={norm:.8f}") |
| print(f"patches={patches.shape} (unnormalized)") |
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|
|
| if __name__ == "__main__": |
| main() |
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