2025-03-17 12:04:08 -04:00

99 lines
3.5 KiB
Python

from pathlib import Path
import typer
from tenacity import retry, stop_after_attempt, wait_fixed
from typing_extensions import Annotated
from .exporters.constants import DELETE_PATTERNS, SOURCE_TO_METADATA, ModelSource
from .exporters.onnx import export as onnx_export
from .exporters.rknn import export as rknn_export
app = typer.Typer(pretty_exceptions_show_locals=False)
def generate_readme(model_name: str, model_source: ModelSource) -> str:
(name, link, type) = SOURCE_TO_METADATA[model_source]
match model_source:
case ModelSource.MCLIP:
tags = ["immich", "clip", "multilingual"]
case ModelSource.OPENCLIP:
tags = ["immich", "clip"]
lowered = model_name.lower()
if "xlm" in lowered or "nllb" in lowered:
tags.append("multilingual")
case ModelSource.INSIGHTFACE:
tags = ["immich", "facial-recognition"]
case _:
raise ValueError(f"Unsupported model source {model_source}")
return f"""---
tags:
{" - " + "\n - ".join(tags)}
---
# Model Description
This repo contains ONNX exports for the associated {type} model by {name}. See the [{name}]({link}) repo for more info.
This repo is specifically intended for use with [Immich](https://immich.app/), a self-hosted photo library.
"""
@app.command()
def main(
model_name: str,
model_source: ModelSource,
output_dir: Path = Path("./models"),
no_cache: bool = False,
hf_organization: str = "immich-app",
hf_auth_token: Annotated[str | None, typer.Option(envvar="HF_AUTH_TOKEN")] = None,
) -> None:
hf_model_name = model_name.split("/")[-1]
hf_model_name = hf_model_name.replace("xlm-roberta-large", "XLM-Roberta-Large")
hf_model_name = hf_model_name.replace("xlm-roberta-base", "XLM-Roberta-Base")
output_dir = output_dir / hf_model_name
match model_source:
case ModelSource.MCLIP | ModelSource.OPENCLIP:
output_dir.mkdir(parents=True, exist_ok=True)
onnx_export(model_name, model_source, output_dir, no_cache=no_cache)
case ModelSource.INSIGHTFACE:
from huggingface_hub import snapshot_download
# TODO: start from insightface dump instead of downloading from HF
snapshot_download(f"immich-app/{hf_model_name}", local_dir=output_dir)
case _:
raise ValueError(f"Unsupported model source {model_source}")
try:
rknn_export(output_dir, no_cache=no_cache)
except Exception as e:
print(f"Failed to export model {model_name} to rknn: {e}")
(output_dir / "rknpu").unlink(missing_ok=True)
readme_path = output_dir / "README.md"
if no_cache or not readme_path.exists():
with open(readme_path, "w") as f:
f.write(generate_readme(model_name, model_source))
if hf_auth_token is not None:
from huggingface_hub import create_repo, upload_folder
repo_id = f"{hf_organization}/{hf_model_name}"
@retry(stop=stop_after_attempt(5), wait=wait_fixed(5))
def upload_model() -> None:
create_repo(repo_id, exist_ok=True, token=hf_auth_token)
upload_folder(
repo_id=repo_id,
folder_path=output_dir,
# remote repo files to be deleted before uploading
# deletion is in the same commit as the upload, so it's atomic
delete_patterns=DELETE_PATTERNS,
token=hf_auth_token,
)
upload_model()
if __name__ == "__main__":
typer.run(main)