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https://github.com/immich-app/immich.git
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87a0ba3db3
* export clip models * export to hf refactored export code * export mclip, general refactoring cleanup * updated conda deps * do transforms with pillow and numpy, add tokenization config to export, general refactoring * moved conda dockerfile, re-added poetry * minor fixes * updated link * updated tests * removed `requirements.txt` from workflow * fixed mimalloc path * removed torchvision * cleaner np typing * review suggestions * update default model name * update test
39 lines
1.3 KiB
Python
39 lines
1.3 KiB
Python
from pathlib import Path
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import onnx
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import onnxruntime as ort
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import onnxsim
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def optimize_onnxsim(model_path: Path | str, output_path: Path | str) -> None:
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model_path = Path(model_path)
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output_path = Path(output_path)
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model = onnx.load(model_path.as_posix())
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model, check = onnxsim.simplify(model, skip_shape_inference=True)
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assert check, "Simplified ONNX model could not be validated"
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onnx.save(model, output_path.as_posix())
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def optimize_ort(
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model_path: Path | str,
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output_path: Path | str,
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level: ort.GraphOptimizationLevel = ort.GraphOptimizationLevel.ORT_ENABLE_BASIC,
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) -> None:
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model_path = Path(model_path)
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output_path = Path(output_path)
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sess_options = ort.SessionOptions()
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sess_options.graph_optimization_level = level
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sess_options.optimized_model_filepath = output_path.as_posix()
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ort.InferenceSession(model_path.as_posix(), providers=["CPUExecutionProvider"], sess_options=sess_options)
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def optimize(model_path: Path | str) -> None:
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model_path = Path(model_path)
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optimize_ort(model_path, model_path)
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# onnxsim serializes large models as a blob, which uses much more memory when loading the model at runtime
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if not any(file.name.startswith("Constant") for file in model_path.parent.iterdir()):
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optimize_onnxsim(model_path, model_path)
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