Mercurial > repos > goeckslab > extract_embeddings

annotate pytorch_embedding.xml @ 1:84f96c952c2c draft default tip

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planemo upload for repository https://github.com/goeckslab/gleam.git commit 5b6cd961948137853177b14b0fff80a5d40e8a07
author goeckslab
date Sun, 09 Nov 2025 19:03:21 +0000
parents 38333676a029
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38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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1 <tool id="extract_embeddings" name="Image Embedding Extraction" version="1.0.0">
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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2 <description>Extract image embeddings using a deep learning model</description>
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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3
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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4 <requirements>
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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5 <container type="docker">quay.io/goeckslab/galaxy-ludwig-gpu:0.10.1</container>
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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6 </requirements>
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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7 <stdio>
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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8 <exit_code range="137" level="fatal_oom" description="Out of Memory" />
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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9 <exit_code range="1:" level="fatal" description="Error occurred. Please check Tool Standard Error" />
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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10 </stdio>
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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11 <command><![CDATA[
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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12 mkdir -p "./hf_cache" &&
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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13 export HF_HOME="./hf_cache" &&
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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14 export TORCH_HOME="./hf_cache" &&
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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15 python $__tool_directory__/pytorch_embedding.py
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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16 --zip_file "$input_zip"
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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17 --output_csv "$output_csv"
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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18 --model_name "$model_name"
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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19 #if $apply_normalization
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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20 --normalize
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21 #end if
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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22 #if $ludwig_format
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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23 --ludwig_format
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24 #end if
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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25 --transform_type "$transform_type"
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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26 ]]></command>
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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27 <configfiles>
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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28 <inputs name="inputs" />
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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29 </configfiles>
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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30 <inputs>
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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31 <param argument="input_zip" type="data" format="zip" label="Input Zip File (Images)" help="Provide a zip file containing images to process." />
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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32 <param argument="model_name" type="select" label="Model for Embedding Extraction" help="Select the model to use for embedding extraction.">
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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33 <option value="alexnet">AlexNet</option>
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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34 <option value="convnext_tiny">ConvNeXt-Tiny</option>
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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35 <option value="convnext_small">ConvNeXt-Small</option>
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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36 <option value="convnext_base">ConvNeXt-Base</option>
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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37 <option value="convnext_large">ConvNeXt-Large</option>
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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38 <option value="densenet121">DenseNet121</option>
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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39 <option value="densenet161">DenseNet161</option>
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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40 <option value="densenet169">DenseNet169</option>
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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41 <option value="densenet201">DenseNet201</option>
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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42 <option value="efficientnet_b0" >EfficientNet-B0</option>
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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43 <option value="efficientnet_b1">EfficientNet-B1</option>
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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44 <option value="efficientnet_b2">EfficientNet-B2</option>
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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45 <option value="efficientnet_b3">EfficientNet-B3</option>
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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46 <option value="efficientnet_b4">EfficientNet-B4</option>
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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47 <option value="efficientnet_b5">EfficientNet-B5</option>
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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48 <option value="efficientnet_b6">EfficientNet-B6</option>
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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49 <option value="efficientnet_b7">EfficientNet-B7</option>
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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50 <option value="efficientnet_v2_s">EfficientNetV2-S</option>
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51 <option value="efficientnet_v2_m">EfficientNetV2-M</option>
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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52 <option value="efficientnet_v2_l">EfficientNetV2-L</option>
1
84f96c952c2c planemo upload for repository https://github.com/goeckslab/gleam.git commit 5b6cd961948137853177b14b0fff80a5d40e8a07
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53 <option value="gpfm">GPFM (Generalizable Pathology Foundation Model)</option>
0
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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54 <option value="googlenet">GoogLeNet</option>
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55 <option value="inception_v3">Inception-V3</option>
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56 <option value="mnasnet0_5">MNASNet-0.5</option>
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57 <option value="mnasnet0_75">MNASNet-0.75</option>
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58 <option value="mnasnet1_0">MNASNet-1.0</option>
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59 <option value="mnasnet1_3">MNASNet-1.3</option>
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60 <option value="mobilenet_v2">MobileNetV2</option>
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61 <option value="mobilenet_v3_large">MobileNetV3-Large</option>
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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62 <option value="mobilenet_v3_small">MobileNetV3-Small</option>
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63 <option value="regnet_x_400mf">RegNet-X-400MF</option>
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64 <option value="regnet_x_800mf">RegNet-X-800MF</option>
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65 <option value="regnet_x_1_6gf">RegNet-X-1.6GF</option>
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66 <option value="regnet_x_3_2gf">RegNet-X-3.2GF</option>
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67 <option value="regnet_x_8gf">RegNet-X-8GF</option>
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68 <option value="resnet18">ResNet-18</option>
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69 <option value="resnet34">ResNet-34</option>
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70 <option value="resnet50" selected="true">ResNet-50</option>
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71 <option value="resnet101">ResNet-101</option>
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72 <option value="resnet152">ResNet-152</option>
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73 <option value="resnext50_32x4d">ResNeXt-50-32x4d</option>
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74 <option value="resnext101_32x8d">ResNeXt-101-32x8d</option>
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75 <option value="shufflenet_v2_x0_5">ShuffleNetV2-0.5x</option>
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76 <option value="shufflenet_v2_x1_0">ShuffleNetV2-1.0x</option>
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77 <option value="squeezenet1_0">SqueezeNet1.0</option>
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78 <option value="squeezenet1_1">SqueezeNet1.1</option>
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79 <option value="swin_b">Swin-B</option>
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80 <option value="swin_s">Swin-S</option>
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81 <option value="swin_t">Swin-T</option>
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82 <option value="vgg11">VGG-11</option>
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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83 <option value="vgg13">VGG-13</option>
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84 <option value="vgg16">VGG-16</option>
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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85 <option value="vgg19">VGG-19</option>
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86 <option value="vit_b_16">ViT-B-16</option>
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87 <option value="vit_b_32">ViT-B-32</option>
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88 <option value="wide_resnet50_2">Wide-ResNet50-2</option>
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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89 <option value="wide_resnet101_2">Wide-ResNet101-2</option>
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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90 </param>
38333676a029 planemo upload for repository https://github.com/goeckslab/gleam.git commit f57ec1ad637e8299db265ee08be0fa9d4d829b93
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91 <param argument="apply_normalization" type="boolean" label="Apply Normalization" help="Enable or disable normalization of embeddings." checked="true"/>
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92 <param argument="transform_type" type="select" label="Image Transformation Type" help="Choose the transformation type to apply before extraction.">
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93 <option value="RGB" selected="true">RGB</option>
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94 <option value="grayscale">Grayscale</option>
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95 <option value="rgba_to_rgb">RGBA to RGB</option>
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96 <option value="clahe">CLAHE (Contrast Limited Adaptive Histogram Equalization)</option>
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97 <option value="edges">Edge Detection</option>
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98 </param>
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99 <param name="ludwig_format" type="boolean" optional="true" label="Convert vectors (stored as columns) into a single string column (Ludwig Format)?"/>
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100 </inputs>
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101 <outputs>
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102 <data name="output_csv" format="csv" label="Extracted Embeddings" />
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103 </outputs>
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104
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105 <tests>
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106 <test>
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107 <param name="input_zip" value="1_digit.zip" ftype="zip" />
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108 <param name="model_name" value="resnet50" />
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109 <param name="apply_normalization" value="true" />
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110 <param name="transform_type" value="RGB" />
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111 <output name="output_csv">
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112 <assert_contents>
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113 <has_text text="sample_name" />
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114 <has_n_columns min="1" />
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115 </assert_contents>
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116 </output>
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117 </test>
1
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118 <test>
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119 <param name="input_zip" value="1_digit.zip" ftype="zip" />
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120 <param name="model_name" value="gpfm" />
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121 <param name="apply_normalization" value="true" />
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122 <param name="transform_type" value="RGB" />
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123 <output name="output_csv">
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124 <assert_contents>
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125 <has_text text="sample_name" />
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126 <has_n_columns min="1" />
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127 </assert_contents>
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128 </output>
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129 </test>
0
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130 </tests>
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131 <help>
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132 <![CDATA[
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133 **What it does**
1
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134 This tool extracts image embeddings using a selected deep learning model, including specialized pathology models like GPFM.
0
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135
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136 **Inputs**
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137 - A zip file containing images to process.
1
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138 - A model selection for embedding extraction (includes GPFM for pathology images).
0
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139 - An option to apply normalization to the extracted embeddings.
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140 - A choice of image transformation type before processing.
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141
1
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142 **Models Available**
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143 - Standard computer vision models (ResNet, EfficientNet, ViT, etc.)
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144 - GPFM: Generalizable Pathology Foundation Model - specialized for medical/pathology images
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145 * Automatically downloads 1.2GB pretrained weights on first use
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146 * Uses DinoVisionTransformer architecture (1024-dimensional embeddings)
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147 * Optimized for histopathology images at 224x224 resolution
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148
0
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149 **Outputs**
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150 - A CSV file containing embeddings. Each row corresponds to an image, with the file name in the first column and embedding vectors in the subsequent columns.
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151 ]]>
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152 </help>
1
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153 </tool>