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docs: model benchmarks (#17036)
* model benchmarks * minor fixes * formatting * docs build * maybe fix reference * clarify optimal * use emojis * wording * wording * clarify optimal wording * bolding * more detailed instructions * clarify edge case fix * early exit in dim loop
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Mert
GitHub
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16 changed files with 2209 additions and 255 deletions
docs/docs
machine-learning
.gitignore
export
immich_model_exporter
pyproject.tomluv.lock|
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@ -262,7 +262,7 @@ No, this is not supported. Only models listed in the [Hugging Face][huggingface]
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### I want to be able to search in other languages besides English. How can I do that?
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You can change to a multilingual CLIP model. See [here](/docs/features/searching#clip-model) for instructions.
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You can change to a multilingual CLIP model. See [here](/docs/features/searching#clip-models) for instructions.
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### Does Immich support Facial Recognition for videos?
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Load diff
3
machine-learning/.gitignore
vendored
3
machine-learning/.gitignore
vendored
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@ -12,6 +12,9 @@ onnx__*
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*.pos_embed
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vocab.txt
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export/immich_model_exporter/models/**/README.md
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export/**/results/*.json
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export/**/root
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*.armnn
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tokenizer.json
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tokenizer_config.json
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special_tokens_map.json
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@ -0,0 +1,165 @@
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import json
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import resource
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from pathlib import Path
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import typer
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from tenacity import retry, stop_after_attempt, wait_fixed
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from typing_extensions import Annotated
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from .exporters.constants import DELETE_PATTERNS, SOURCE_TO_METADATA, ModelSource, ModelTask
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from .exporters.onnx import export as onnx_export
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from .exporters.rknn import export as rknn_export
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app = typer.Typer(pretty_exceptions_show_locals=False)
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def generate_readme(model_name: str, model_source: ModelSource) -> str:
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(name, link, type) = SOURCE_TO_METADATA[model_source]
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match model_source:
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case ModelSource.MCLIP:
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tags = ["immich", "clip", "multilingual"]
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case ModelSource.OPENCLIP:
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tags = ["immich", "clip"]
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lowered = model_name.lower()
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if "xlm" in lowered or "nllb" in lowered:
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tags.append("multilingual")
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case ModelSource.INSIGHTFACE:
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tags = ["immich", "facial-recognition"]
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case _:
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raise ValueError(f"Unsupported model source {model_source}")
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return f"""---
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tags:
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{" - " + "\n - ".join(tags)}
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---
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# Model Description
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This repo contains ONNX exports for the associated {type} model by {name}. See the [{name}]({link}) repo for more info.
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This repo is specifically intended for use with [Immich](https://immich.app/), a self-hosted photo library.
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"""
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def clean_name(model_name: str) -> str:
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hf_model_name = model_name.split("/")[-1]
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hf_model_name = hf_model_name.replace("xlm-roberta-large", "XLM-Roberta-Large")
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hf_model_name = hf_model_name.replace("xlm-roberta-base", "XLM-Roberta-Base")
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return hf_model_name
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@app.command()
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def export(model_name: str, model_source: ModelSource, output_dir: Path = Path("models"), cache: bool = True) -> None:
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hf_model_name = clean_name(model_name)
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output_dir = output_dir / hf_model_name
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match model_source:
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case ModelSource.MCLIP | ModelSource.OPENCLIP:
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output_dir.mkdir(parents=True, exist_ok=True)
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onnx_export(model_name, model_source, output_dir, cache=cache)
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case ModelSource.INSIGHTFACE:
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from huggingface_hub import snapshot_download
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# TODO: start from insightface dump instead of downloading from HF
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snapshot_download(f"immich-app/{hf_model_name}", local_dir=output_dir)
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case _:
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raise ValueError(f"Unsupported model source {model_source}")
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try:
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rknn_export(output_dir, cache=cache)
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except Exception as e:
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print(f"Failed to export model {model_name} to rknn: {e}")
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(output_dir / "rknpu").unlink(missing_ok=True)
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readme_path = output_dir / "README.md"
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if not (cache or readme_path.exists()):
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with open(readme_path, "w") as f:
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f.write(generate_readme(model_name, model_source))
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@app.command()
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def profile(model_dir: Path, model_task: ModelTask, output_path: Path) -> None:
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from timeit import timeit
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import numpy as np
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import onnxruntime as ort
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np.random.seed(0)
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sess_options = ort.SessionOptions()
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sess_options.enable_cpu_mem_arena = False
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providers = ["CPUExecutionProvider"]
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provider_options = [{"arena_extend_strategy": "kSameAsRequested"}]
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match model_task:
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case ModelTask.SEARCH:
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textual = ort.InferenceSession(
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model_dir / "textual" / "model.onnx",
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sess_options=sess_options,
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providers=providers,
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provider_options=provider_options,
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)
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tokens = {node.name: np.random.rand(*node.shape).astype(np.int32) for node in textual.get_inputs()}
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visual = ort.InferenceSession(
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model_dir / "visual" / "model.onnx",
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sess_options=sess_options,
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providers=providers,
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provider_options=provider_options,
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)
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image = {node.name: np.random.rand(*node.shape).astype(np.float32) for node in visual.get_inputs()}
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def predict() -> None:
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textual.run(None, tokens)
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visual.run(None, image)
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case ModelTask.FACIAL_RECOGNITION:
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detection = ort.InferenceSession(
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model_dir / "detection" / "model.onnx",
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sess_options=sess_options,
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providers=providers,
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provider_options=provider_options,
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)
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image = {node.name: np.random.rand(1, 3, 640, 640).astype(np.float32) for node in detection.get_inputs()}
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recognition = ort.InferenceSession(
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model_dir / "recognition" / "model.onnx",
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sess_options=sess_options,
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providers=providers,
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provider_options=provider_options,
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)
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face = {node.name: np.random.rand(1, 3, 112, 112).astype(np.float32) for node in recognition.get_inputs()}
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def predict() -> None:
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detection.run(None, image)
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recognition.run(None, face)
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case _:
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raise ValueError(f"Unsupported model task {model_task}")
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predict()
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ms = timeit(predict, number=100)
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rss = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss
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json.dump({"pretrained_model": model_dir.name, "peak_rss": rss, "exec_time_ms": ms}, output_path.open("w"))
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print(f"Model {model_dir.name} took {ms:.2f}ms per iteration using {rss / 1024:.2f}MiB of memory")
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@app.command()
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def upload(
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model_dir: Path,
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hf_organization: str = "immich-app",
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hf_auth_token: Annotated[str | None, typer.Option(envvar="HF_AUTH_TOKEN")] = None,
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) -> None:
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from huggingface_hub import create_repo, upload_folder
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repo_id = f"{hf_organization}/{model_dir.name}"
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@retry(stop=stop_after_attempt(5), wait=wait_fixed(5))
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def upload_model() -> None:
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create_repo(repo_id, exist_ok=True, token=hf_auth_token)
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upload_folder(
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repo_id=repo_id,
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folder_path=model_dir,
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# remote repo files to be deleted before uploading
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# deletion is in the same commit as the upload, so it's atomic
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delete_patterns=DELETE_PATTERNS,
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token=hf_auth_token,
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)
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upload_model()
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@ -0,0 +1,3 @@
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from immich_model_exporter import app
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app()
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@ -1,98 +0,0 @@
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from pathlib import Path
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import typer
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from tenacity import retry, stop_after_attempt, wait_fixed
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from typing_extensions import Annotated
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from .exporters.constants import DELETE_PATTERNS, SOURCE_TO_METADATA, ModelSource
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from .exporters.onnx import export as onnx_export
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from .exporters.rknn import export as rknn_export
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app = typer.Typer(pretty_exceptions_show_locals=False)
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def generate_readme(model_name: str, model_source: ModelSource) -> str:
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(name, link, type) = SOURCE_TO_METADATA[model_source]
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match model_source:
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case ModelSource.MCLIP:
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tags = ["immich", "clip", "multilingual"]
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case ModelSource.OPENCLIP:
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tags = ["immich", "clip"]
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lowered = model_name.lower()
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if "xlm" in lowered or "nllb" in lowered:
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tags.append("multilingual")
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case ModelSource.INSIGHTFACE:
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tags = ["immich", "facial-recognition"]
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case _:
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raise ValueError(f"Unsupported model source {model_source}")
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return f"""---
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tags:
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{" - " + "\n - ".join(tags)}
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---
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# Model Description
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This repo contains ONNX exports for the associated {type} model by {name}. See the [{name}]({link}) repo for more info.
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This repo is specifically intended for use with [Immich](https://immich.app/), a self-hosted photo library.
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"""
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@app.command()
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def main(
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model_name: str,
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model_source: ModelSource,
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output_dir: Path = Path("./models"),
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no_cache: bool = False,
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hf_organization: str = "immich-app",
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hf_auth_token: Annotated[str | None, typer.Option(envvar="HF_AUTH_TOKEN")] = None,
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) -> None:
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hf_model_name = model_name.split("/")[-1]
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hf_model_name = hf_model_name.replace("xlm-roberta-large", "XLM-Roberta-Large")
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hf_model_name = hf_model_name.replace("xlm-roberta-base", "XLM-Roberta-Base")
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output_dir = output_dir / hf_model_name
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match model_source:
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case ModelSource.MCLIP | ModelSource.OPENCLIP:
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output_dir.mkdir(parents=True, exist_ok=True)
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onnx_export(model_name, model_source, output_dir, no_cache=no_cache)
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case ModelSource.INSIGHTFACE:
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from huggingface_hub import snapshot_download
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# TODO: start from insightface dump instead of downloading from HF
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snapshot_download(f"immich-app/{hf_model_name}", local_dir=output_dir)
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case _:
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raise ValueError(f"Unsupported model source {model_source}")
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try:
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rknn_export(output_dir, no_cache=no_cache)
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except Exception as e:
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print(f"Failed to export model {model_name} to rknn: {e}")
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(output_dir / "rknpu").unlink(missing_ok=True)
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readme_path = output_dir / "README.md"
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if no_cache or not readme_path.exists():
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with open(readme_path, "w") as f:
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f.write(generate_readme(model_name, model_source))
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if hf_auth_token is not None:
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from huggingface_hub import create_repo, upload_folder
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repo_id = f"{hf_organization}/{hf_model_name}"
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@retry(stop=stop_after_attempt(5), wait=wait_fixed(5))
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def upload_model() -> None:
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create_repo(repo_id, exist_ok=True, token=hf_auth_token)
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upload_folder(
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repo_id=repo_id,
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folder_path=output_dir,
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# remote repo files to be deleted before uploading
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# deletion is in the same commit as the upload, so it's atomic
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delete_patterns=DELETE_PATTERNS,
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token=hf_auth_token,
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)
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upload_model()
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if __name__ == "__main__":
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typer.run(main)
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@ -8,6 +8,11 @@ class ModelSource(StrEnum):
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OPENCLIP = "openclip"
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class ModelTask(StrEnum):
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FACIAL_RECOGNITION = "facial-recognition"
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SEARCH = "clip"
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class SourceMetadata(NamedTuple):
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name: str
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link: str
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@ -22,6 +27,13 @@ SOURCE_TO_METADATA = {
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),
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}
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SOURCE_TO_TASK = {
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ModelSource.MCLIP: ModelTask.SEARCH,
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ModelSource.OPENCLIP: ModelTask.SEARCH,
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ModelSource.INSIGHTFACE: ModelTask.FACIAL_RECOGNITION,
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}
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RKNN_SOCS = ["rk3566", "rk3568", "rk3576", "rk3588"]
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@ -5,16 +5,16 @@ from .models import mclip, openclip
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def export(
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model_name: str, model_source: ModelSource, output_dir: Path, opset_version: int = 19, no_cache: bool = False
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model_name: str, model_source: ModelSource, output_dir: Path, opset_version: int = 19, cache: bool = True
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) -> None:
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visual_dir = output_dir / "visual"
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textual_dir = output_dir / "textual"
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match model_source:
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case ModelSource.MCLIP:
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mclip.to_onnx(model_name, opset_version, visual_dir, textual_dir, no_cache=no_cache)
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mclip.to_onnx(model_name, opset_version, visual_dir, textual_dir, cache=cache)
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case ModelSource.OPENCLIP:
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name, _, pretrained = model_name.partition("__")
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config = openclip.OpenCLIPModelConfig(name, pretrained)
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openclip.to_onnx(config, opset_version, visual_dir, textual_dir, no_cache=no_cache)
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openclip.to_onnx(config, opset_version, visual_dir, textual_dir, cache=cache)
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case _:
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raise ValueError(f"Unsupported model source {model_source}")
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@ -19,10 +19,10 @@ def to_onnx(
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opset_version: int,
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output_dir_visual: Path | str,
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output_dir_textual: Path | str,
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no_cache: bool = False,
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cache: bool = True,
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) -> tuple[Path, Path]:
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textual_path = get_model_path(output_dir_textual)
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if no_cache or not textual_path.exists():
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if not cache or not textual_path.exists():
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import torch
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from multilingual_clip.pt_multilingual_clip import MultilingualCLIP
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from transformers import AutoTokenizer
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@ -39,9 +39,7 @@ def to_onnx(
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_export_text_encoder(model, textual_path, opset_version)
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else:
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print(f"Model {textual_path} already exists, skipping")
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visual_path, _ = openclip_to_onnx(
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_MCLIP_TO_OPENCLIP[model_name], opset_version, output_dir_visual, no_cache=no_cache
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)
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visual_path, _ = openclip_to_onnx(_MCLIP_TO_OPENCLIP[model_name], opset_version, output_dir_visual, cache=cache)
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assert visual_path is not None, "Visual model export failed"
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return visual_path, textual_path
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|
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@ -37,7 +37,7 @@ def to_onnx(
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opset_version: int,
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output_dir_visual: Path | str | None = None,
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output_dir_textual: Path | str | None = None,
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no_cache: bool = False,
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cache: bool = True,
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) -> tuple[Path | None, Path | None]:
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visual_path = None
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textual_path = None
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@ -49,9 +49,7 @@ def to_onnx(
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output_dir_textual = Path(output_dir_textual)
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textual_path = get_model_path(output_dir_textual)
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if not no_cache and (
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(textual_path is None or textual_path.exists()) and (visual_path is None or visual_path.exists())
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):
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if cache and ((textual_path is None or textual_path.exists()) and (visual_path is None or visual_path.exists())):
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print(f"Models {textual_path} and {visual_path} already exist, skipping")
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return visual_path, textual_path
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@ -75,7 +73,7 @@ def to_onnx(
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param.requires_grad_(False)
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if visual_path is not None and output_dir_visual is not None:
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if no_cache or not visual_path.exists():
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if not cache or not visual_path.exists():
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save_config(
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open_clip.get_model_preprocess_cfg(model),
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output_dir_visual / "preprocess_cfg.json",
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@ -86,7 +84,7 @@ def to_onnx(
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print(f"Model {visual_path} already exists, skipping")
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if textual_path is not None and output_dir_textual is not None:
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if no_cache or not textual_path.exists():
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if not cache or not textual_path.exists():
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tokenizer_name = text_vision_cfg["text_cfg"].get("hf_tokenizer_name", "openai/clip-vit-base-patch32")
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AutoTokenizer.from_pretrained(tokenizer_name).save_pretrained(output_dir_textual)
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_export_text_encoder(model, model_cfg, textual_path, opset_version)
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|
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@ -9,13 +9,13 @@ def _export_platform(
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inputs: list[str] | None = None,
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input_size_list: list[list[int]] | None = None,
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fuse_matmul_softmax_matmul_to_sdpa: bool = True,
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no_cache: bool = False,
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cache: bool = True,
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) -> None:
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from rknn.api import RKNN
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input_path = model_dir / "model.onnx"
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output_path = model_dir / "rknpu" / target_platform / "model.rknn"
|
||||
if not no_cache and output_path.exists():
|
||||
if cache and output_path.exists():
|
||||
print(f"Model {input_path} already exists at {output_path}, skipping")
|
||||
return
|
||||
|
||||
|
|
@ -49,7 +49,7 @@ def _export_platforms(
|
|||
model_dir: Path,
|
||||
inputs: list[str] | None = None,
|
||||
input_size_list: list[list[int]] | None = None,
|
||||
no_cache: bool = False,
|
||||
cache: bool = True,
|
||||
) -> None:
|
||||
fuse_matmul_softmax_matmul_to_sdpa = True
|
||||
for soc in RKNN_SOCS:
|
||||
|
|
@ -60,7 +60,7 @@ def _export_platforms(
|
|||
inputs=inputs,
|
||||
input_size_list=input_size_list,
|
||||
fuse_matmul_softmax_matmul_to_sdpa=fuse_matmul_softmax_matmul_to_sdpa,
|
||||
no_cache=no_cache,
|
||||
cache=cache,
|
||||
)
|
||||
except Exception as e:
|
||||
print(f"Failed to export model for {soc}: {e}")
|
||||
|
|
@ -73,24 +73,24 @@ def _export_platforms(
|
|||
inputs=inputs,
|
||||
input_size_list=input_size_list,
|
||||
fuse_matmul_softmax_matmul_to_sdpa=fuse_matmul_softmax_matmul_to_sdpa,
|
||||
no_cache=no_cache,
|
||||
cache=cache,
|
||||
)
|
||||
|
||||
|
||||
def export(model_dir: Path, no_cache: bool = False) -> None:
|
||||
def export(model_dir: Path, cache: bool = True) -> None:
|
||||
textual = model_dir / "textual"
|
||||
visual = model_dir / "visual"
|
||||
detection = model_dir / "detection"
|
||||
recognition = model_dir / "recognition"
|
||||
|
||||
if textual.is_dir():
|
||||
_export_platforms(textual, no_cache=no_cache)
|
||||
_export_platforms(textual, cache=cache)
|
||||
|
||||
if visual.is_dir():
|
||||
_export_platforms(visual, no_cache=no_cache)
|
||||
_export_platforms(visual, cache=cache)
|
||||
|
||||
if detection.is_dir():
|
||||
_export_platforms(detection, inputs=["input.1"], input_size_list=[[1, 3, 640, 640]], no_cache=no_cache)
|
||||
_export_platforms(detection, inputs=["input.1"], input_size_list=[[1, 3, 640, 640]], cache=cache)
|
||||
|
||||
if recognition.is_dir():
|
||||
_export_platforms(recognition, inputs=["input.1"], input_size_list=[[1, 3, 112, 112]], no_cache=no_cache)
|
||||
_export_platforms(recognition, inputs=["input.1"], input_size_list=[[1, 3, 112, 112]], cache=cache)
|
||||
|
|
|
|||
22
machine-learning/export/immich_model_exporter/get_dims.py
Normal file
22
machine-learning/export/immich_model_exporter/get_dims.py
Normal file
|
|
@ -0,0 +1,22 @@
|
|||
import json
|
||||
from pathlib import Path
|
||||
|
||||
models_dir = Path("models")
|
||||
model_to_embed_dim = {}
|
||||
for model_dir in models_dir.iterdir():
|
||||
if not model_dir.is_dir():
|
||||
continue
|
||||
|
||||
config_path = model_dir / "config.json"
|
||||
if not config_path.exists():
|
||||
print(f"Skipping {model_dir.name} as it does not have a config.json")
|
||||
continue
|
||||
with open(config_path) as f:
|
||||
config = json.load(f)
|
||||
embed_dim = config.get("embed_dim")
|
||||
if embed_dim is None:
|
||||
print(f"Skipping {model_dir.name} as it does not have an embed_dim")
|
||||
continue
|
||||
print(f"{model_dir.name}: {embed_dim}")
|
||||
model_to_embed_dim[model_dir.name] = {"dimSize": embed_dim}
|
||||
print(json.dumps(model_to_embed_dim))
|
||||
121
machine-learning/export/immich_model_exporter/parse_eval_data.py
Normal file
121
machine-learning/export/immich_model_exporter/parse_eval_data.py
Normal file
|
|
@ -0,0 +1,121 @@
|
|||
import polars as pl
|
||||
|
||||
|
||||
def collapsed_table(language: str, df: pl.DataFrame) -> str:
|
||||
with pl.Config(
|
||||
tbl_formatting="ASCII_MARKDOWN",
|
||||
tbl_hide_column_data_types=True,
|
||||
tbl_hide_dataframe_shape=True,
|
||||
fmt_str_lengths=100,
|
||||
tbl_rows=1000,
|
||||
tbl_width_chars=1000,
|
||||
):
|
||||
return f"<details>\n<summary>{language}</summary>\n{str(df)}\n</details>"
|
||||
|
||||
|
||||
languages = {
|
||||
"en": "English",
|
||||
"ar": "Arabic",
|
||||
"bn": "Bengali",
|
||||
"zh": "Chinese (Simplified)",
|
||||
"hr": "Croatian",
|
||||
"quz": "Cusco Quechua",
|
||||
"cs": "Czech",
|
||||
"da": "Danish",
|
||||
"nl": "Dutch",
|
||||
"fil": "Filipino",
|
||||
"fi": "Finnish",
|
||||
"fr": "French",
|
||||
"de": "German",
|
||||
"el": "Greek",
|
||||
"he": "Hebrew",
|
||||
"hi": "Hindi",
|
||||
"hu": "Hungarian",
|
||||
"id": "Indonesian",
|
||||
"it": "Italian",
|
||||
"ja": "Japanese",
|
||||
"ko": "Korean",
|
||||
"mi": "Maori",
|
||||
"no": "Norwegian",
|
||||
"fa": "Persian",
|
||||
"pl": "Polish",
|
||||
"pt": "Portuguese",
|
||||
"ro": "Romanian",
|
||||
"ru": "Russian",
|
||||
"es": "Spanish",
|
||||
"sw": "Swahili",
|
||||
"sv": "Swedish",
|
||||
"te": "Telugu",
|
||||
"th": "Thai",
|
||||
"tr": "Turkish",
|
||||
"uk": "Ukrainian",
|
||||
"vi": "Vietnamese",
|
||||
}
|
||||
|
||||
profile_df = pl.scan_ndjson("profiling/*.json").select("pretrained_model", "peak_rss", "exec_time_ms")
|
||||
eval_df = pl.scan_ndjson("results/*.json").select("model", "pretrained", "language", "metrics")
|
||||
|
||||
eval_df = eval_df.with_columns(
|
||||
model=pl.col("model")
|
||||
.str.replace("xlm-roberta-base", "XLM-Roberta-Base")
|
||||
.str.replace("xlm-roberta-large", "XLM-Roberta-Large")
|
||||
)
|
||||
eval_df = eval_df.with_columns(pretrained_model=pl.concat_str(pl.col("model"), pl.col("pretrained"), separator="__"))
|
||||
eval_df = eval_df.drop("model", "pretrained")
|
||||
eval_df = eval_df.join(profile_df, on="pretrained_model")
|
||||
|
||||
eval_df = eval_df.with_columns(
|
||||
recall=(
|
||||
pl.col("metrics").struct.field("image_retrieval_recall@1")
|
||||
+ pl.col("metrics").struct.field("image_retrieval_recall@5")
|
||||
+ pl.col("metrics").struct.field("image_retrieval_recall@10")
|
||||
)
|
||||
* (100 / 3)
|
||||
)
|
||||
|
||||
pareto_front = eval_df.join_where(
|
||||
eval_df.select("language", "peak_rss", "exec_time_ms", "recall").rename(
|
||||
{
|
||||
"language": "language_other",
|
||||
"peak_rss": "peak_rss_other",
|
||||
"exec_time_ms": "exec_time_ms_other",
|
||||
"recall": "recall_other",
|
||||
}
|
||||
),
|
||||
(pl.col("language") == pl.col("language_other"))
|
||||
& (pl.col("peak_rss_other") <= pl.col("peak_rss"))
|
||||
& (pl.col("exec_time_ms_other") <= pl.col("exec_time_ms"))
|
||||
& (pl.col("recall_other") >= pl.col("recall"))
|
||||
& (
|
||||
(pl.col("peak_rss_other") < pl.col("peak_rss"))
|
||||
| (pl.col("exec_time_ms_other") < pl.col("exec_time_ms"))
|
||||
| (pl.col("recall_other") > pl.col("recall"))
|
||||
),
|
||||
)
|
||||
eval_df = eval_df.join(pareto_front, on=["pretrained_model", "language"], how="left")
|
||||
eval_df = eval_df.with_columns(is_pareto=pl.col("recall_other").is_null())
|
||||
eval_df = (
|
||||
eval_df.drop("peak_rss_other", "exec_time_ms_other", "recall_other", "language_other")
|
||||
.unique(subset=["pretrained_model", "language"])
|
||||
.collect()
|
||||
)
|
||||
eval_df.write_parquet("model_info.parquet")
|
||||
|
||||
eval_df = eval_df.drop("metrics")
|
||||
eval_df = eval_df.filter(pl.col("recall") >= 20)
|
||||
eval_df = eval_df.sort("recall", descending=True)
|
||||
eval_df = eval_df.select(
|
||||
pl.col("pretrained_model").alias("Model"),
|
||||
(pl.col("peak_rss") / 1024).round().cast(pl.UInt32).alias("Memory (MiB)"),
|
||||
pl.col("exec_time_ms").round(2).alias("Execution Time (ms)"),
|
||||
pl.col("language").alias("Language"),
|
||||
pl.col("recall").round(2).alias("Recall (%)"),
|
||||
pl.when(pl.col("is_pareto")).then(pl.lit("✅")).otherwise(pl.lit("❌")).alias("Pareto Optimal"),
|
||||
)
|
||||
|
||||
|
||||
for language in languages:
|
||||
lang_df = eval_df.filter(pl.col("Language") == language).drop("Language")
|
||||
if lang_df.shape[0] == 0:
|
||||
continue
|
||||
print(collapsed_table(languages[language], lang_df))
|
||||
|
|
@ -1,7 +1,11 @@
|
|||
import subprocess
|
||||
from pathlib import Path
|
||||
|
||||
from exporters.constants import ModelSource
|
||||
|
||||
from immich_model_exporter import clean_name
|
||||
from immich_model_exporter.exporters.constants import SOURCE_TO_TASK
|
||||
|
||||
mclip = [
|
||||
"M-CLIP/LABSE-Vit-L-14",
|
||||
"M-CLIP/XLM-Roberta-Large-Vit-B-16Plus",
|
||||
|
|
@ -74,10 +78,28 @@ insightface = [
|
|||
|
||||
|
||||
def export_models(models: list[str], source: ModelSource) -> None:
|
||||
profiling_dir = Path("profiling")
|
||||
profiling_dir.mkdir(exist_ok=True)
|
||||
for model in models:
|
||||
try:
|
||||
print(f"Exporting model {model}")
|
||||
subprocess.check_call(["python", "-m", "immich_model_exporter.export", model, source])
|
||||
model_dir = f"models/{clean_name(model)}"
|
||||
task = SOURCE_TO_TASK[source]
|
||||
|
||||
print(f"Processing model {model}")
|
||||
subprocess.check_call(["python", "-m", "immich_model_exporter", "export", model, source])
|
||||
subprocess.check_call(
|
||||
[
|
||||
"python",
|
||||
"-m",
|
||||
"immich_model_exporter",
|
||||
"profile",
|
||||
model_dir,
|
||||
task,
|
||||
"--output_path",
|
||||
profiling_dir / f"{model}.json",
|
||||
]
|
||||
)
|
||||
subprocess.check_call(["python", "-m", "immich_model_exporter", "upload", model_dir])
|
||||
except Exception as e:
|
||||
print(f"Failed to export model {model}: {e}")
|
||||
|
||||
|
|
@ -86,3 +108,64 @@ if __name__ == "__main__":
|
|||
export_models(mclip, ModelSource.MCLIP)
|
||||
export_models(openclip, ModelSource.OPENCLIP)
|
||||
export_models(insightface, ModelSource.INSIGHTFACE)
|
||||
|
||||
Path("results").mkdir(exist_ok=True)
|
||||
subprocess.check_call(
|
||||
[
|
||||
"python",
|
||||
"clip_benchmark",
|
||||
"eval",
|
||||
"--pretrained_model",
|
||||
*[name.replace("__", ",") for name in openclip],
|
||||
"--task",
|
||||
"zeroshot_retrieval",
|
||||
"--dataset",
|
||||
"crossmodal3600",
|
||||
"--batch_size",
|
||||
"64",
|
||||
"--language",
|
||||
"ar",
|
||||
"bn",
|
||||
"cs",
|
||||
"da",
|
||||
"de",
|
||||
"el",
|
||||
"en",
|
||||
"es",
|
||||
"fa",
|
||||
"fi",
|
||||
"fil",
|
||||
"fr",
|
||||
"he",
|
||||
"hi",
|
||||
"hr",
|
||||
"hu",
|
||||
"id",
|
||||
"it",
|
||||
"ja",
|
||||
"ko",
|
||||
"mi",
|
||||
"nl",
|
||||
"no",
|
||||
"pl",
|
||||
"pt",
|
||||
"quz",
|
||||
"ro",
|
||||
"ru",
|
||||
"sv",
|
||||
"sw",
|
||||
"te",
|
||||
"th",
|
||||
"tr",
|
||||
"uk",
|
||||
"vi",
|
||||
"zh",
|
||||
"--recall_k",
|
||||
"1",
|
||||
"5",
|
||||
"10",
|
||||
"--no_amp",
|
||||
"--output",
|
||||
"results/{dataset}_{language}_{model}_{pretrained}.json",
|
||||
]
|
||||
)
|
||||
|
|
|
|||
|
|
@ -14,6 +14,8 @@ dependencies = [
|
|||
"rknn-toolkit2>=2.3.0",
|
||||
"transformers>=4.49.0",
|
||||
"tenacity>=9.0.0",
|
||||
"clip-benchmark>=1.6.1",
|
||||
"polars>=1.25.2",
|
||||
]
|
||||
|
||||
[dependency-groups]
|
||||
|
|
@ -27,15 +29,6 @@ override-dependencies = [
|
|||
"torchvision>=0.21",
|
||||
]
|
||||
|
||||
[tool.uv.sources]
|
||||
torch = [{ index = "pytorch-cpu" }]
|
||||
torchvision = [{ index = "pytorch-cpu" }]
|
||||
|
||||
[[tool.uv.index]]
|
||||
name = "pytorch-cpu"
|
||||
url = "https://download.pytorch.org/whl/cpu"
|
||||
explicit = true
|
||||
|
||||
[tool.hatch.build.targets.sdist]
|
||||
include = ["immich_model_exporter"]
|
||||
|
||||
|
|
|
|||
755
machine-learning/export/uv.lock
generated
755
machine-learning/export/uv.lock
generated
|
|
@ -17,8 +17,8 @@ resolution-markers = [
|
|||
overrides = [
|
||||
{ name = "onnx", specifier = ">=1.16.0,<2" },
|
||||
{ name = "onnxruntime", specifier = ">=1.18.2,<2" },
|
||||
{ name = "torch", specifier = ">=2.4", index = "https://download.pytorch.org/whl/cpu" },
|
||||
{ name = "torchvision", specifier = ">=0.21", index = "https://download.pytorch.org/whl/cpu" },
|
||||
{ name = "torch", specifier = ">=2.4" },
|
||||
{ name = "torchvision", specifier = ">=0.21" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -55,6 +55,15 @@ wheels = [
|
|||
{ url = "https://files.pythonhosted.org/packages/09/71/54e999902aed72baf26bca0d50781b01838251a462612966e9fc4891eadd/black-25.1.0-py3-none-any.whl", hash = "sha256:95e8176dae143ba9097f351d174fdaf0ccd29efb414b362ae3fd72bf0f710717", size = 207646 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "braceexpand"
|
||||
version = "0.1.7"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/54/93/badd4f5ccf25209f3fef2573073da9fe4a45a3da99fca2f800f942130c0f/braceexpand-0.1.7.tar.gz", hash = "sha256:e6e539bd20eaea53547472ff94f4fb5c3d3bf9d0a89388c4b56663aba765f705", size = 7777 }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/fa/93/e8c04e80e82391a6e51f218ca49720f64236bc824e92152a2633b74cf7ab/braceexpand-0.1.7-py2.py3-none-any.whl", hash = "sha256:91332d53de7828103dcae5773fb43bc34950b0c8160e35e0f44c4427a3b85014", size = 5923 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "certifi"
|
||||
version = "2025.1.31"
|
||||
|
|
@ -137,6 +146,25 @@ wheels = [
|
|||
{ url = "https://files.pythonhosted.org/packages/7e/d4/7ebdbd03970677812aac39c869717059dbb71a4cfc033ca6e5221787892c/click-8.1.8-py3-none-any.whl", hash = "sha256:63c132bbbed01578a06712a2d1f497bb62d9c1c0d329b7903a866228027263b2", size = 98188 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "clip-benchmark"
|
||||
version = "1.6.1"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "open-clip-torch" },
|
||||
{ name = "pycocoevalcap" },
|
||||
{ name = "scikit-learn" },
|
||||
{ name = "torch" },
|
||||
{ name = "torchvision" },
|
||||
{ name = "tqdm" },
|
||||
{ name = "transformers" },
|
||||
{ name = "webdataset" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/c4/af/733be824fa6e9b366036388f12893870af84aef8fb06064a5ebc067ac86c/clip_benchmark-1.6.1.tar.gz", hash = "sha256:eb222cec1759bf80458cc9f509c5d01557e63c5f5d8dce4018bf5c0ef3a427e8", size = 2056113 }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/4a/c6/9bb7554f39f9b306b3ce704ad86bd9afe1555a584445a89565b52ba64ddd/clip_benchmark-1.6.1-py2.py3-none-any.whl", hash = "sha256:d83f524d18aaad1cb72bc96d39a2c6f572f34c54e82791a16492d43f77036796", size = 1944703 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "colorama"
|
||||
version = "0.4.6"
|
||||
|
|
@ -158,6 +186,79 @@ wheels = [
|
|||
{ url = "https://files.pythonhosted.org/packages/a7/06/3d6badcf13db419e25b07041d9c7b4a2c331d3f4e7134445ec5df57714cd/coloredlogs-15.0.1-py2.py3-none-any.whl", hash = "sha256:612ee75c546f53e92e70049c9dbfcc18c935a2b9a53b66085ce9ef6a6e5c0934", size = 46018 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "contourpy"
|
||||
version = "1.3.1"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "numpy" },
|
||||
]
|
||||
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|
||||
sdist = { url = "https://files.pythonhosted.org/packages/dc/13/27b4a05a01bcf96e451f624d36d3637101e92b25970295546f7d949b38e9/webdataset-0.2.111.tar.gz", hash = "sha256:5b2835386a25601307a9ded9bcc0dbd1e81a9eee017784152528e77dd8619511", size = 79970 }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/6e/e1/c1140ab6533668930895512ac5cbf07972fa41ebab275f5f5cdd432bc3c7/webdataset-0.2.111-py3-none-any.whl", hash = "sha256:57a70eb5d7029303ce2262d900ee3f16443bb5e9cf25f634775ce972859bcee4", size = 85514 },
|
||||
]
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue