.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/plot_black_op.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_plot_black_op.py: .. _l-black-op: Convert a model with a reduced list of operators ================================================ Some runtime dedicated to onnx do not implement all the operators and a converted model may not run if one of them is missing from the list of available operators. Some converters may convert a model in different ways if the users wants to blacklist some operators. GaussianMixture +++++++++++++++ The first converter to change its behaviour depending on a black list of operators is for model *GaussianMixture*. .. GENERATED FROM PYTHON SOURCE LINES 22-41 .. code-block:: default import onnxruntime import onnx import numpy import os from timeit import timeit import numpy as np import matplotlib.pyplot as plt from onnx.tools.net_drawer import GetPydotGraph, GetOpNodeProducer from onnxruntime import InferenceSession from sklearn.mixture import GaussianMixture from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from skl2onnx import to_onnx data = load_iris() X_train, X_test = train_test_split(data.data) model = GaussianMixture() model.fit(X_train) .. raw:: html

GaussianMixture()

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.. GENERATED FROM PYTHON SOURCE LINES 42-44 Default conversion ++++++++++++++++++ .. GENERATED FROM PYTHON SOURCE LINES 44-60 .. code-block:: default model_onnx = to_onnx( model, X_train[:1].astype(np.float32), options={id(model): {"score_samples": True}}, target_opset=12, ) sess = InferenceSession( model_onnx.SerializeToString(), providers=["CPUExecutionProvider"] ) xt = X_test[:5].astype(np.float32) print(model.score_samples(xt)) print(sess.run(None, {"X": xt})[2]) .. rst-class:: sphx-glr-script-out .. code-block:: none [-1.55507181 -1.88300778 -3.57222331 -1.67405519 -1.85984688] [[-1.5550716] [-1.8830082] [-3.572221 ] [-1.6740558] [-1.8598464]] .. GENERATED FROM PYTHON SOURCE LINES 61-62 Display the ONNX graph. .. GENERATED FROM PYTHON SOURCE LINES 62-81 .. code-block:: default pydot_graph = GetPydotGraph( model_onnx.graph, name=model_onnx.graph.name, rankdir="TB", node_producer=GetOpNodeProducer( "docstring", color="yellow", fillcolor="yellow", style="filled" ), ) pydot_graph.write_dot("mixture.dot") os.system("dot -O -Gdpi=300 -Tpng mixture.dot") image = plt.imread("mixture.dot.png") fig, ax = plt.subplots(figsize=(40, 20)) ax.imshow(image) ax.axis("off") .. image-sg:: /auto_examples/images/sphx_glr_plot_black_op_001.png :alt: plot black op :srcset: /auto_examples/images/sphx_glr_plot_black_op_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none (-0.5, 4796.5, 8425.5, -0.5) .. GENERATED FROM PYTHON SOURCE LINES 82-88 Conversion without ReduceLogSumExp ++++++++++++++++++++++++++++++++++ Parameter *black_op* is used to tell the converter not to use this operator. Let's see what the converter produces in that case. .. GENERATED FROM PYTHON SOURCE LINES 88-104 .. code-block:: default model_onnx2 = to_onnx( model, X_train[:1].astype(np.float32), options={id(model): {"score_samples": True}}, black_op={"ReduceLogSumExp"}, target_opset=12, ) sess2 = InferenceSession( model_onnx2.SerializeToString(), providers=["CPUExecutionProvider"] ) xt = X_test[:5].astype(np.float32) print(model.score_samples(xt)) print(sess2.run(None, {"X": xt})[2]) .. rst-class:: sphx-glr-script-out .. code-block:: none [-1.55507181 -1.88300778 -3.57222331 -1.67405519 -1.85984688] [[-1.5550716] [-1.8830082] [-3.5722215] [-1.6740558] [-1.8598464]] .. GENERATED FROM PYTHON SOURCE LINES 105-106 Display the ONNX graph. .. GENERATED FROM PYTHON SOURCE LINES 106-125 .. code-block:: default pydot_graph = GetPydotGraph( model_onnx2.graph, name=model_onnx2.graph.name, rankdir="TB", node_producer=GetOpNodeProducer( "docstring", color="yellow", fillcolor="yellow", style="filled" ), ) pydot_graph.write_dot("mixture2.dot") os.system("dot -O -Gdpi=300 -Tpng mixture2.dot") image = plt.imread("mixture2.dot.png") fig, ax = plt.subplots(figsize=(40, 20)) ax.imshow(image) ax.axis("off") .. image-sg:: /auto_examples/images/sphx_glr_plot_black_op_002.png :alt: plot black op :srcset: /auto_examples/images/sphx_glr_plot_black_op_002.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none (-0.5, 4921.5, 13264.5, -0.5) .. GENERATED FROM PYTHON SOURCE LINES 126-128 Processing time +++++++++++++++ .. GENERATED FROM PYTHON SOURCE LINES 128-143 .. code-block:: default print( timeit( stmt="sess.run(None, {'X': xt})", number=10000, globals={"sess": sess, "xt": xt} ) ) print( timeit( stmt="sess2.run(None, {'X': xt})", number=10000, globals={"sess2": sess2, "xt": xt}, ) ) .. rst-class:: sphx-glr-script-out .. code-block:: none 0.4618227000000843 0.5505055000000993 .. GENERATED FROM PYTHON SOURCE LINES 144-145 The model using ReduceLogSumExp is much faster. .. GENERATED FROM PYTHON SOURCE LINES 147-154 If the converter cannot convert without... ++++++++++++++++++++++++++++++++++++++++++ Many converters do not consider the white and black lists of operators. If a converter fails to convert without using a blacklisted operator (or only whitelisted operators), *skl2onnx* raises an error. .. GENERATED FROM PYTHON SOURCE LINES 154-167 .. code-block:: default try: to_onnx( model, X_train[:1].astype(np.float32), options={id(model): {"score_samples": True}}, black_op={"ReduceLogSumExp", "Add"}, target_opset=12, ) except RuntimeError as e: print("Error:", e) .. rst-class:: sphx-glr-script-out .. code-block:: none Error: Operator 'Add' is black listed. .. GENERATED FROM PYTHON SOURCE LINES 168-169 **Versions used for this example** .. GENERATED FROM PYTHON SOURCE LINES 169-179 .. code-block:: default import sklearn # noqa print("numpy:", numpy.__version__) print("scikit-learn:", sklearn.__version__) import skl2onnx # noqa print("onnx: ", onnx.__version__) print("onnxruntime: ", onnxruntime.__version__) print("skl2onnx: ", skl2onnx.__version__) .. rst-class:: sphx-glr-script-out .. code-block:: none numpy: 1.23.5 scikit-learn: 1.4.dev0 onnx: 1.15.0 onnxruntime: 1.16.0+cu118 skl2onnx: 1.15.0 .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 31.602 seconds) .. _sphx_glr_download_auto_examples_plot_black_op.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_black_op.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_black_op.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_