feat(ml): ML on Rockchip NPUs (#15241)

machine-learning/export/immich_model_exporter/export.py

@@ -0,0 +1,98 @@
+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)