comparison ms2deepscore_training.xml @ 0:0a0529822d91 draft default tip

planemo upload for repository https://github.com/RECETOX/galaxytools/tree/master/tools/ms2deepscore commit 4bd610e0cbbcbed51a6bfb880179777fc8034fd6

0:0a0529822d91

<tool id="ms2deepscore_training" name="MS2DeepScore Model Training" version="@TOOL_VERSION@+galaxy0">
    <description>Compute similarity scores using a pre-trained MS2DeepScore model</description>
    <macros>
        <import>macros.xml</import>
    </macros>
    <expand macro="creator"/>
    <expand macro="edam" />

    <requirements>
        <requirement type="package" version="@TOOL_VERSION@">ms2deepscore</requirement>
        <requirement type="package" version="@ONNX_VERSION@">onnx</requirement>
    </requirements>
    
    <command detect_errors="exit_code"><![CDATA[
        mkdir processing;
        cp $spectra processing/input."$spectra.ext";
        python3 ${python_wrapper}
    ]]></command>
<configfiles>
<configfile name="python_wrapper">
import onnx
import os
import torch
from ms2deepscore.models import load_model
from ms2deepscore.SettingsMS2Deepscore import SettingsMS2Deepscore
from ms2deepscore.wrapper_functions.training_wrapper_functions import train_ms2deepscore_wrapper, StoreTrainingData

@json_load@

settings = SettingsMS2Deepscore(**model_params)
file = "processing/input.$spectra.ext"
directory = train_ms2deepscore_wrapper(file, settings, $validation_split_fraction)

expected_file_names = StoreTrainingData(file)
pt_model_path = os.path.join(expected_file_names.trained_models_folder, directory, settings.model_file_name)

model = load_model(pt_model_path)
model.eval()

batch_size = 1
number_of_bins = settings.number_of_bins()
additional_inputs = len(settings.additional_metadata)

# Create dummy inputs
spectra_tensors_1 = torch.randn(batch_size, number_of_bins)
spectra_tensors_2 = torch.randn(batch_size, number_of_bins)
metadata_1 = torch.randn(batch_size, additional_inputs)
metadata_2 = torch.randn(batch_size, additional_inputs)

# Export the model to ONNX
torch.onnx.export(
    model,
    (spectra_tensors_1, spectra_tensors_2, metadata_1, metadata_2),
    "$onnx_trained_model",
    verbose=True
)

</configfile>
</configfiles>
    
    <inputs>
        <expand macro="training_param" />
    </inputs>
    
    <outputs>
        <data label="Trained model" name="onnx_trained_model" format="onnx"/>
    </outputs>

    <tests>
        <test expect_num_outputs="1">
            <param name="spectra" value="clean_spectra.mgf" ftype="mgf"/>
            <param name="model_param" value="Model_Parameter_JSON.json" ftype="json" />
            <param name="validation_split_fraction" value="5"/>
            <output name="onnx_trained_model" value="Trained_model.onnx" ftype="onnx" compare="sim_size"/>
        </test>
    </tests>

    <help>
<![CDATA[
Info
====
This tool trains a MS2DeepScore model using the provided training data and model configuration.
The trained model is then exported using ONNX.

About
=====
@HELP@
]]>
    </help>
    <expand macro="citations"/>
</tool>