ms2deepscore-onnx-converter 0.2.0

A CLI tool to convert ms2deepscore PyTorch models to ONNX

ms2deepscore pytorch 2 onnx converter

This library/cli tool aims at converting ms2deepscore models from pytorch to onnx.

Usage with uvx

uvx ms2deepscore-onnx-converter https://zenodo.org/records/17826815 -o onnx_model_dir
# or
uvx ms2deepscore-onnx-converter YOUR_MS2DEEPSCORE_MODEL.pt

Installation

uv add ms2deepscore-onnx-converter
# or using pip:
pip install ms2deepscore-onnx-converter

Usage

The tool can either convert a local ms2deepscore model or download one from zenodo and convert it to onnx.

# Using the CLI:
ms2ds_onnx https://zenodo.org/records/17826815 -o onnx_model_dir
ms2ds_onnx ms2deepscore_model.pt -o onnx_model_dir

# or within your python script:
from ms2ds_converter import convert_to_onnx

convert_to_onnx("ms2deepscore_model.pt", "onnx_model_dir")

Inference using onnx runtime

After converting a ms2deepscore model to pytorch you can use the ONNX Runtime for inference. You'll need to install onnxruntime separately.

import json

import numpy as np
import onnxruntime as ort
from matchms import Spectrum
from matchms.importing import load_spectra
from ms2deepscore import SettingsMS2Deepscore
from ms2deepscore.tensorize_spectra import tensorize_spectra

def compute_embeddings_onnx(
    onnx_session: ort.InferenceSession,
    spectra: list[Spectrum],
    settings: SettingsMS2Deepscore,
) -> np.ndarray:
    # We use ms2deepscore to create tensors from spectra and convert them to np arrays.
    X_binned_torch, X_metadata_torch = tensorize_spectra(spectra, settings)
    X_binned = X_binned_torch.numpy().astype(np.float32)
    X_metadata = X_metadata_torch.numpy().astype(np.float32)

    # Build the input data, depending on additional metadata in model, e.g. ["input_peaks", "input_metadata"] or just ["input_peaks"].
    input_names = [inp.name for inp in onnx_session.get_inputs()]
    output_name = onnx_session.get_outputs()[0].name

    input_feed = {input_names[0]: X_binned}

    if len(input_names) > 1 and X_metadata.shape[1] > 0:
        input_feed[input_names[1]] = X_metadata

    # inference step.
    embeddings = onnx_session.run([output_name], input_feed)[0]

    return embeddings


def main():
    # load some spectra with matchms and maybe do some filtering...
    spectra = list(load_spectra("spectra.mgf"))

    # Load exported SettingsMS2Deepscore.
    with open(
        "onnx_model_dir/ms2deepscore_model_settings.json", "r", encoding="utf-8"
    ) as file:
        settings_dict = json.load(file)

    # Remove spectrum_file_path from settings for inference, since it will fail validation.
    settings_dict["spectrum_file_path"] = None
    settings = SettingsMS2Deepscore(**settings_dict)

    # Load ONNX Model and compute embeddings, will use either GPU or CPU as fallback.
    ort_session = ort.InferenceSession(
        "onnx_model_dir/ms2deepscore_model.onnx",
        providers=["CUDAExecutionProvider", "CPUExecutionProvider"]
    )
    embeddings = compute_embeddings_onnx(ort_session, spectra, settings)

    # Use your embeddings in some way...
    np.save("embeddings_onnx.npy", embeddings)


if __name__ == "__main__":
    main()

License

GNU GPLv3. See License