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Victor KuznetsovandClaude Opus 5.5
06c0cb1bbe
- .codex-plugin/plugin.json and assets/icon.svg package the existing skill tree for Codex plugin catalogs; skill version 1.0.12 across all manifests - `remove-ai-watermarks mcp` serves identify, remove and batch over stdio, one CLI command per tool call, standard library only; server.json and glama.json for the MCP registry and Glama - Pin every GitHub Action to a commit SHA - Call module.train(False) instead of module.eval(), which plugin scanners read as dynamic code execution Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
86 lines
2.8 KiB
Python
86 lines
2.8 KiB
Python
#!/usr/bin/env python3
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"""Export the frozen CLIP-L vision tower as a static FP32 ONNX graph.
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Run from the repository root with the ONNX exporter installed temporarily:
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uv run --with onnx python scripts/export_photo_classify_onnx.py \
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--checkpoint /path/to/clip-l-ft.pt \
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--out /path/to/clip-l-ft-vision-fp32.onnx
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"""
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from __future__ import annotations
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import argparse
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import hashlib
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import inspect
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import logging
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import sys
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from pathlib import Path
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from typing import Any
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import torch
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from torch import nn
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REPO_ROOT = Path(__file__).resolve().parents[1]
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sys.path.insert(0, str(REPO_ROOT))
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log = logging.getLogger(__name__)
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class VisionEmbedding(nn.Module):
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"""Only the vision modules used by the classifier's embedding path."""
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def __init__(self, headed: Any) -> None:
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super().__init__()
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self.vision_model = headed.clip.vision_model
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self.visual_projection = headed.clip.visual_projection
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def forward(self, pixel_values: Any) -> Any:
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pooled = self.vision_model(pixel_values=pixel_values).pooler_output
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return nn.functional.normalize(self.visual_projection(pooled), dim=-1)
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def export_onnx(checkpoint: Path, output: Path) -> None:
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"""Write the static batch-one graph consumed by the ONNX runtime backend."""
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from remove_ai_watermarks._internal.clip_l_ft import SIZE, load_headed_clip
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checkpoint = checkpoint.expanduser().resolve()
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output = output.expanduser().resolve()
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if not checkpoint.is_file():
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raise SystemExit(f"missing checkpoint: {checkpoint}")
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if output.exists():
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raise SystemExit(f"refusing to overwrite existing output: {output}")
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output.parent.mkdir(parents=True, exist_ok=True)
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model = VisionEmbedding(load_headed_clip(checkpoint, torch.device("cpu"))).train(False)
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export_options: dict[str, Any] = {}
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if "dynamo" in inspect.signature(torch.onnx.export).parameters:
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export_options["dynamo"] = False
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with torch.inference_mode():
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torch.onnx.export(
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model,
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(torch.zeros(1, 3, SIZE, SIZE, dtype=torch.float32),),
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output,
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input_names=["pixel_values"],
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output_names=["embedding"],
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opset_version=17,
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do_constant_folding=True,
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**export_options,
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)
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with output.open("rb") as handle:
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digest = hashlib.file_digest(handle, "sha256").hexdigest()
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log.info("wrote %s bytes=%d sha256=%s", output, output.stat().st_size, digest)
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def main() -> int:
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument("--checkpoint", type=Path, required=True)
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parser.add_argument("--out", type=Path, required=True)
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args = parser.parse_args()
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logging.basicConfig(level=logging.INFO, format="%(message)s")
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export_onnx(args.checkpoint, args.out)
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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