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< li class = "toctree-l1" > < a class = "reference internal" href = "../tutorial.html" > Tutorial< / a > < ul >
< li class = "toctree-l2" > < a class = "reference internal" href = "../tutorial.html#step-1-train-a-model-using-your-favorite-framework" > Step 1: Train a model using your favorite framework< / a > < / li >
< li class = "toctree-l2" > < a class = "reference internal" href = "../tutorial.html#step-2-convert-or-export-the-model-into-onnx-format" > Step 2: Convert or export the model into ONNX format< / a > < / li >
< li class = "toctree-l2" > < a class = "reference internal" href = "../tutorial.html#step-3-load-and-run-the-model-using-onnx-runtime" > Step 3: Load and run the model using ONNX Runtime< / a > < / li >
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< li class = "toctree-l2" > < a class = "reference internal" href = "../api_summary.html#examples-and-datasets" > Examples and datasets< / a > < / li >
< li class = "toctree-l2" > < a class = "reference internal" href = "../api_summary.html#load-and-run-a-model" > Load and run a model< / a > < / li >
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< li class = "toctree-l2 current" > < a class = "current reference internal" href = "#" > Draw a pipeline< / a > < / li >
< li class = "toctree-l2" > < a class = "reference internal" href = "plot_load_and_predict.html" > Load and predict with ONNX Runtime and a very simple model< / a > < / li >
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< li class = "toctree-l2" > < a class = "reference internal" href = "plot_convert_pipeline_vectorizer.html" > Train, convert and predict with ONNX Runtime< / a > < / li >
< li class = "toctree-l2" > < a class = "reference internal" href = "plot_common_errors.html" > Common errors with onnxruntime< / a > < / li >
< li class = "toctree-l2" > < a class = "reference internal" href = "plot_train_convert_predict.html" > Train, convert and predict with ONNX Runtime< / a > < / li >
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< div class = "sphx-glr-download-link-note admonition note" >
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< p class = "admonition-title" > Note< / p >
< p > Click < a class = "reference internal" href = "#sphx-glr-download-auto-examples-plot-pipeline-py" > < span class = "std std-ref" > here< / span > < / a > to download the full example code< / p >
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< div class = "sphx-glr-example-title section" id = "draw-a-pipeline" >
< span id = "sphx-glr-auto-examples-plot-pipeline-py" > < / span > < h1 > Draw a pipeline< a class = "headerlink" href = "#draw-a-pipeline" title = "Permalink to this headline" > ¶< / a > < / h1 >
< p > There is no other way to look into one model stored
in ONNX format than looking into its node with
< em > onnx< / em > . This example demonstrates
how to draw a model and to retrieve it in < em > json< / em >
format.< / p >
< div class = "contents local topic" id = "contents" >
< ul class = "simple" >
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< li > < p > < a class = "reference internal" href = "#retrieve-a-model-in-json-format" id = "id1" > Retrieve a model in JSON format< / a > < / p > < / li >
< li > < p > < a class = "reference internal" href = "#draw-a-model-with-onnx" id = "id2" > Draw a model with ONNX< / a > < / p > < / li >
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< / ul >
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< div class = "section" id = "retrieve-a-model-in-json-format" >
< h2 > < a class = "toc-backref" href = "#id1" > Retrieve a model in JSON format< / a > < a class = "headerlink" href = "#retrieve-a-model-in-json-format" title = "Permalink to this headline" > ¶< / a > < / h2 >
< p > That’ < / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > from< / span > < span class = "nn" > onnxruntime.datasets< / span > < span class = "k" > import< / span > < span class = "n" > get_example< / span >
< span class = "n" > example1< / span > < span class = "o" > =< / span > < span class = "n" > get_example< / span > < span class = "p" > (< / span > < span class = "s2" > " mul_1.onnx" < / span > < span class = "p" > )< / span >
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< span class = "kn" > import< / span > < span class = "nn" > onnx< / span >
< span class = "n" > model< / span > < span class = "o" > =< / span > < span class = "n" > onnx< / span > < span class = "o" > .< / span > < span class = "n" > load< / span > < span class = "p" > (< / span > < span class = "n" > example1< / span > < span class = "p" > )< / span > < span class = "c1" > # model is a ModelProto protobuf message< / span >
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< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "n" > model< / span > < span class = "p" > )< / span >
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< / pre > < / div >
< / div >
< p class = "sphx-glr-script-out" > Out:< / p >
< div class = "sphx-glr-script-out highlight-none notranslate" > < div class = "highlight" > < pre > < span > < / span > producer_name: " chenta"
graph {
node {
input: " X"
input: " W"
output: " Y"
name: " mul_1"
op_type: " Mul"
}
name: " mul test"
initializer {
dims: 3
dims: 2
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data_type: 1
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float_data: 1.0
float_data: 2.0
float_data: 3.0
float_data: 4.0
float_data: 5.0
float_data: 6.0
name: " W"
}
input {
name: " X"
type {
tensor_type {
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elem_type: 1
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shape {
dim {
dim_value: 3
}
dim {
dim_value: 2
}
}
}
}
}
output {
name: " Y"
type {
tensor_type {
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elem_type: 1
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shape {
dim {
dim_value: 3
}
dim {
dim_value: 2
}
}
}
}
}
}
opset_import {
domain: " "
version: 7
}
< / pre > < / div >
< / div >
< / div >
< div class = "section" id = "draw-a-model-with-onnx" >
< h2 > < a class = "toc-backref" href = "#id2" > Draw a model with ONNX< / a > < a class = "headerlink" href = "#draw-a-model-with-onnx" title = "Permalink to this headline" > ¶< / a > < / h2 >
< p > We use < a class = "reference external" href = "https://github.com/onnx/onnx/blob/master/onnx/tools/net_drawer.py" > net_drawer.py< / a >
included in < em > onnx< / em > package.
We use < em > onnx< / em > to load the model
in a different way than before.< / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > from< / span > < span class = "nn" > onnx< / span > < span class = "k" > import< / span > < span class = "n" > ModelProto< / span >
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< span class = "n" > model< / span > < span class = "o" > =< / span > < span class = "n" > ModelProto< / span > < span class = "p" > ()< / span >
< span class = "k" > with< / span > < span class = "nb" > open< / span > < span class = "p" > (< / span > < span class = "n" > example1< / span > < span class = "p" > ,< / span > < span class = "s1" > ' rb' < / span > < span class = "p" > )< / span > < span class = "k" > as< / span > < span class = "n" > fid< / span > < span class = "p" > :< / span >
< span class = "n" > content< / span > < span class = "o" > =< / span > < span class = "n" > fid< / span > < span class = "o" > .< / span > < span class = "n" > read< / span > < span class = "p" > ()< / span >
< span class = "n" > model< / span > < span class = "o" > .< / span > < span class = "n" > ParseFromString< / span > < span class = "p" > (< / span > < span class = "n" > content< / span > < span class = "p" > )< / span >
< / pre > < / div >
< / div >
< p > We convert it into a graph.< / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > from< / span > < span class = "nn" > onnx.tools.net_drawer< / span > < span class = "k" > import< / span > < span class = "n" > GetPydotGraph< / span > < span class = "p" > ,< / span > < span class = "n" > GetOpNodeProducer< / span >
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< span class = "n" > pydot_graph< / span > < span class = "o" > =< / span > < span class = "n" > GetPydotGraph< / span > < span class = "p" > (< / span > < span class = "n" > model< / span > < span class = "o" > .< / span > < span class = "n" > graph< / span > < span class = "p" > ,< / span > < span class = "n" > name< / span > < span class = "o" > =< / span > < span class = "n" > model< / span > < span class = "o" > .< / span > < span class = "n" > graph< / span > < span class = "o" > .< / span > < span class = "n" > name< / span > < span class = "p" > ,< / span > < span class = "n" > rankdir< / span > < span class = "o" > =< / span > < span class = "s2" > " LR" < / span > < span class = "p" > ,< / span >
< span class = "n" > node_producer< / span > < span class = "o" > =< / span > < span class = "n" > GetOpNodeProducer< / span > < span class = "p" > (< / span > < span class = "s2" > " docstring" < / span > < span class = "p" > ))< / span >
< span class = "n" > pydot_graph< / span > < span class = "o" > .< / span > < span class = "n" > write_dot< / span > < span class = "p" > (< / span > < span class = "s2" > " graph.dot" < / span > < span class = "p" > )< / span >
< / pre > < / div >
< / div >
< p > Then into an image< / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > import< / span > < span class = "nn" > os< / span >
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< span class = "n" > os< / span > < span class = "o" > .< / span > < span class = "n" > system< / span > < span class = "p" > (< / span > < span class = "s1" > ' dot -O -Tpng graph.dot' < / span > < span class = "p" > )< / span >
< / pre > < / div >
< / div >
< p > Which we display…< / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > import< / span > < span class = "nn" > matplotlib.pyplot< / span > < span class = "k" > as< / span > < span class = "nn" > plt< / span >
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< span class = "n" > image< / span > < span class = "o" > =< / span > < span class = "n" > plt< / span > < span class = "o" > .< / span > < span class = "n" > imread< / span > < span class = "p" > (< / span > < span class = "s2" > " graph.dot.png" < / span > < span class = "p" > )< / span >
< span class = "n" > plt< / span > < span class = "o" > .< / span > < span class = "n" > imshow< / span > < span class = "p" > (< / span > < span class = "n" > image< / span > < span class = "p" > )< / span >
< / pre > < / div >
< / div >
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< p > < a class = "reference download internal" download = "" href = "../_downloads/d4b7e2e2374529ffe1a9a01ca58630da/plot_pipeline.py" > < code class = "xref download docutils literal notranslate" > < span class = "pre" > Download< / span > < span class = "pre" > Python< / span > < span class = "pre" > source< / span > < span class = "pre" > code:< / span > < span class = "pre" > plot_pipeline.py< / span > < / code > < / a > < / p >
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< p > < a class = "reference download internal" download = "" href = "../_downloads/d9d626ddf304487a15edfdc3e6b03d3e/plot_pipeline.ipynb" > < code class = "xref download docutils literal notranslate" > < span class = "pre" > Download< / span > < span class = "pre" > Jupyter< / span > < span class = "pre" > notebook:< / span > < span class = "pre" > plot_pipeline.ipynb< / span > < / code > < / a > < / p >
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