comparison image_learner.xml @ 14:94cd9ac4a9b1 draft default tip

planemo upload for repository https://github.com/goeckslab/gleam.git commit 3188005878e1ad81b0ca59c9c4d4d40dd981acf7

13:1a9c42974a5a

    <description>trains and evaluates an image classification/regression model</description>
    <requirements>
        <container type="docker">quay.io/goeckslab/galaxy-ludwig-gpu:0.10.1</container>
    </requirements>
    <required_files>
        <include path="utils.py" />
        <include path="constants.py" />
        <include path="image_learner_cli.py" />

        <include path="MetaFormer/metaformer_models.py" />
        <include path="MetaFormer/metaformer_stacked_cnn.py" />
        <include path="MetaFormer/__init__.py" />
    </required_files>
    <stdio>
        <exit_code range="137" level="fatal_oom" description="Out of Memory" />
        <exit_code range="1:" level="fatal" description="Error occurred. Please check Tool Standard Error" />
    </stdio>

            echo "Image Learner Classification Experiment is Done!"
        ]]>
    </command>

    <inputs>
        <param name="input_csv" argument="input_csv" type="data" format="csv" optional="false" label="the metadata csv containing image_path column, label column and optional split column" />
        <param name="image_zip" type="data" format="zip" optional="false" label="Image zip" help="Image zip file containing your image data"/>
        <param name="model_name" type="select" optional="false" label="Select a model for your experiment" >

            <option value="resnet18">Resnet18</option>
            <option value="resnet34">Resnet34</option>

            </when>
            <when value="false">
                <!-- No additional parameters to show if the user selects 'No' -->
            </when>
        </conditional>
        <param argument="image_resize"
               name="image_resize"
               type="select"
               label="Image Resize"
               help="Select the size to resize images to. Original size keeps images as-is, while other options resize all images to the specified dimensions.">
            <option value="original" selected="true">Original Size (No Resize)</option>
            <option value="96x96">96x96</option>

            <option value="320x320">320x320</option>
            <option value="384x384">384x384</option>
            <option value="448x448">448x448</option>
            <option value="512x512">512x512</option>
        </param>
        <param argument="augmentation"
               name="augmentation"
               type="select"
               multiple="true"
               display="checkboxes"
               label="Image Augmentation"
               help="Pick any combination of augmentations to apply">

        </test>
        <test expect_num_outputs="3">
            <param name="input_csv" value="mnist_subset.csv" ftype="csv" />
            <param name="image_zip" value="mnist_subset.zip" ftype="zip" />
            <param name="model_name" value="randformer_s12" />
            <param name="customize_defaults" value="true" />
            <param name="epochs" value="5" />
            <output name="output_report">
                <assert_contents>
                    <has_text text="Results Summary" />
                    <has_text text="Train/Validation Results" />
                    <has_text text="Test Results" />

        </test> -->
        <test expect_num_outputs="3">
            <param name="input_csv" value="mnist_subset_binary.csv" ftype="csv" />
            <param name="image_zip" value="mnist_subset.zip" ftype="zip" />
            <param name="model_name" value="resnet18" />
            <param name="customize_defaults" value="true" />
            <param name="threshold" value="0.6" />
            <output name="output_report">
                <assert_contents>
                    <has_text text="Accuracy" />
                    <has_text text="Precision" />
                    <has_text text="Learning Curves Label Accuracy" />

14:94cd9ac4a9b1

    <description>trains and evaluates an image classification/regression model</description>
    <requirements>
        <container type="docker">quay.io/goeckslab/galaxy-ludwig-gpu:0.10.1</container>
    </requirements>
    <required_files>
        <include path="constants.py" />
        <include path="html_structure.py" />
        <include path="image_learner_cli.py" />
        <include path="image_workflow.py" />
        <include path="ludwig_backend.py" />
        <include path="metaformer_setup.py" />
        <include path="plotly_plots.py" />
        <include path="split_data.py" />
        <include path="utils.py" />
        <include path="MetaFormer/__init__.py" />
        <include path="MetaFormer/metaformer_models.py" />
        <include path="MetaFormer/metaformer_stacked_cnn.py" />
    </required_files>
    <stdio>
        <exit_code range="137" level="fatal_oom" description="Out of Memory" />
        <exit_code range="1:" level="fatal" description="Error occurred. Please check Tool Standard Error" />
    </stdio>

            echo "Image Learner Classification Experiment is Done!"
        ]]>
    </command>

    <inputs>
        <param name="input_csv" type="data" format="csv" optional="false" label="the metadata csv containing image_path column, label column and optional split column" />
        <param name="image_zip" type="data" format="zip" optional="false" label="Image zip" help="Image zip file containing your image data"/>
        <param name="model_name" type="select" optional="false" label="Select a model for your experiment" >

            <option value="resnet18">Resnet18</option>
            <option value="resnet34">Resnet34</option>

            </when>
            <when value="false">
                <!-- No additional parameters to show if the user selects 'No' -->
            </when>
        </conditional>
        <param name="image_resize"
               type="select"
               label="Image Resize"
               help="Select the size to resize images to. Original size keeps images as-is, while other options resize all images to the specified dimensions.">
            <option value="original" selected="true">Original Size (No Resize)</option>
            <option value="96x96">96x96</option>

            <option value="320x320">320x320</option>
            <option value="384x384">384x384</option>
            <option value="448x448">448x448</option>
            <option value="512x512">512x512</option>
        </param>
        <param name="augmentation"
               type="select"
               multiple="true"
               display="checkboxes"
               label="Image Augmentation"
               help="Pick any combination of augmentations to apply">

        </test>
        <test expect_num_outputs="3">
            <param name="input_csv" value="mnist_subset.csv" ftype="csv" />
            <param name="image_zip" value="mnist_subset.zip" ftype="zip" />
            <param name="model_name" value="randformer_s12" />
            <param name="advanced_settings|customize_defaults" value="true" />
            <param name="advanced_settings|epochs" value="5" />
            <output name="output_report">
                <assert_contents>
                    <has_text text="Results Summary" />
                    <has_text text="Train/Validation Results" />
                    <has_text text="Test Results" />

        </test> -->
        <test expect_num_outputs="3">
            <param name="input_csv" value="mnist_subset_binary.csv" ftype="csv" />
            <param name="image_zip" value="mnist_subset.zip" ftype="zip" />
            <param name="model_name" value="resnet18" />
            <param name="advanced_settings|customize_defaults" value="true" />
            <param name="advanced_settings|threshold" value="0.6" />
            <output name="output_report">
                <assert_contents>
                    <has_text text="Accuracy" />
                    <has_text text="Precision" />
                    <has_text text="Learning Curves Label Accuracy" />