comparison bioimage_inference.xml @ 5:37b9ead209da draft default tip

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/bioimaging commit 399a62ba78a6a3d7885c7df03cc7de37e1a7ec6f

4:2b61d8fcfa52

<tool id="bioimage_inference" name="Process image using a BioImage.IO model" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="23.0">
    <description>with PyTorch</description>
    <macros>
        <token name="@TOOL_VERSION@">2.4.1</token>
        <token name="@VERSION_SUFFIX@">2</token>
    </macros>
    <creator>
        <organization name="European Galaxy Team" url="https://galaxyproject.org/eu/" />
        <person givenName="Anup" familyName="Kumar" email="[e-mail]" />
        <person givenName="Beatriz" familyName="Serrano-Solano" email="[e-mail]" />

    <inputs>
        <param name="input_imaging_model" type="data" format="zip" label="BioImage.IO model" help="Please upload a BioImage.IO model."/>
        <param name="input_image_file" type="data" format="tiff,png" label="Input image" help="Please provide an input image for the analysis."/>
        <param name="input_image_input_size" type="text" optional="false" label="Size of the input image" help="Provide the size of the input image. See the chosen model's RDF file to find the correct input size. For example: for the BioImage.IO model MitochondriaEMSegmentationBoundaryModel, the input size is 256 x 256 x 32 x 1. Enter the size as 256,256,32,1."/>
        <param name="input_image_input_axes" type="select" label="Axes of the input image" optional="false" help="Provide the input axes of the input image. See the chosen model's RDF file to find the correct axes. For example: for the BioImage.IO model MitochondriaEMSegmentationBoundaryModel, the input axes is 'bczyx'">
            <option value="bczyx">bczyx</option>
            <option value="bcyx">bcyx</option>
            <option value="byxc">byxc</option>
        </param>
    </inputs>
    <outputs>
        <data format="tiff" name="output_predicted_image" from_work_dir="output_predicted_image.tiff" label="Predicted image"></data>
        <data format="npy" name="output_predicted_image_matrix" from_work_dir="output_predicted_image_matrix.npy" label="Predicted image tensor"></data>

        <test>
            <param name="input_imaging_model" value="input_imaging_model.zip" location="https://zenodo.org/api/records/6647674/files/weights-torchscript.pt/content"/>
            <param name="input_image_file" value="input_image_file.tif" location="https://zenodo.org/api/records/6647674/files/sample_input_0.tif/content"/>
            <param name="input_image_input_size" value="256,256,1,1"/>
            <param name="input_image_input_axes" value="bcyx"/>
            <output name="output_predicted_image" ftype="tiff">
                <assert_contents>
                    <has_size size="524846" delta="110" />
                </assert_contents>
            </output>
            <output name="output_predicted_image_matrix" ftype="npy">
                <assert_contents>
                    <has_size size="524416" delta="110" />
                </assert_contents>
            </output>

        <test>
            <param name="input_imaging_model" value="input_imaging_model.zip" location="https://zenodo.org/api/records/6647674/files/weights-torchscript.pt/content"/>
            <param name="input_image_file" value="input_nucleisegboundarymodel.png"/>
            <param name="input_image_input_size" value="256,256,1,1"/>
            <param name="input_image_input_axes" value="bcyx"/>
            <output name="output_predicted_image" ftype="tiff">
                <assert_contents>
                    <has_size size="524846" delta="110" />
                </assert_contents>
            </output>
            <output name="output_predicted_image_matrix" ftype="npy">
                <assert_contents>
                    <has_size size="524416" delta="110" />
                </assert_contents>
            </output>

5:37b9ead209da

<tool id="bioimage_inference" name="Process image using a BioImage.IO model" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="23.0">
    <description>with PyTorch</description>
    <macros>
        <token name="@TOOL_VERSION@">2.4.1</token>
        <token name="@VERSION_SUFFIX@">3</token>
    </macros>
    <creator>
        <organization name="European Galaxy Team" url="https://galaxyproject.org/eu/" />
        <person givenName="Anup" familyName="Kumar" email="[e-mail]" />
        <person givenName="Beatriz" familyName="Serrano-Solano" email="[e-mail]" />

    <inputs>
        <param name="input_imaging_model" type="data" format="zip" label="BioImage.IO model" help="Please upload a BioImage.IO model."/>
        <param name="input_image_file" type="data" format="tiff,png" label="Input image" help="Please provide an input image for the analysis."/>
        <param name="input_image_input_size" type="text" optional="false" label="Size of the input image" help="Provide the size of the input image. See the chosen model's RDF file to find the correct input size. For example: for the BioImage.IO model MitochondriaEMSegmentationBoundaryModel, the input size is 256 x 256 x 32 x 1. Enter the size as 256,256,32,1."/>
        <param name="input_image_input_axes" type="select" label="Axes of the input image" optional="false" help="Provide the input axes of the input image. See the chosen model's RDF file to find the correct axes. For example: for the BioImage.IO model MitochondriaEMSegmentationBoundaryModel, the input axes is 'bczyx'">
            <option value="bcyx">Four axes (e.g., bcyx, byxc)</option>
            <option value="bczyx">Five axes (e.g., bczyx)</option>
        </param>
    </inputs>
    <outputs>
        <data format="tiff" name="output_predicted_image" from_work_dir="output_predicted_image.tiff" label="Predicted image"></data>
        <data format="npy" name="output_predicted_image_matrix" from_work_dir="output_predicted_image_matrix.npy" label="Predicted image tensor"></data>

        <test>
            <param name="input_imaging_model" value="input_imaging_model.zip" location="https://zenodo.org/api/records/6647674/files/weights-torchscript.pt/content"/>
            <param name="input_image_file" value="input_image_file.tif" location="https://zenodo.org/api/records/6647674/files/sample_input_0.tif/content"/>
            <param name="input_image_input_size" value="256,256,1,1"/>
            <param name="input_image_input_axes" value="bcyx"/>
            <output name="output_predicted_image" ftype="tiff" file="output_nucleisegboundarymodel.tiff" compare="image_diff" />
            <output name="output_predicted_image_matrix" ftype="npy">
                <assert_contents>
                    <has_size size="524416" delta="110" />
                </assert_contents>
            </output>

        <test>
            <param name="input_imaging_model" value="input_imaging_model.zip" location="https://zenodo.org/api/records/6647674/files/weights-torchscript.pt/content"/>
            <param name="input_image_file" value="input_nucleisegboundarymodel.png"/>
            <param name="input_image_input_size" value="256,256,1,1"/>
            <param name="input_image_input_axes" value="bcyx"/>
            <output name="output_predicted_image" ftype="tiff" file="output_nucleisegboundarymodel.tiff" compare="image_diff" />
            <output name="output_predicted_image_matrix" ftype="npy">
                <assert_contents>
                    <has_size size="524416" delta="110" />
                </assert_contents>
            </output>