.. 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 22-42 .. code-block:: Python 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 43-45 Default conversion ++++++++++++++++++ .. GENERATED FROM PYTHON SOURCE LINES 45-61 .. code-block:: Python 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 [-1.68351474 -1.68463982 -2.27655683 -1.68930875 -3.40608478] [[-1.6835146] [-1.6846399] [-2.276558 ] [-1.6893082] [-3.4060864]] .. GENERATED FROM PYTHON SOURCE LINES 62-63 Display the ONNX graph. .. GENERATED FROM PYTHON SOURCE LINES 63-82 .. code-block:: Python 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 (np.float64(-0.5), np.float64(4796.5), np.float64(8425.5), np.float64(-0.5)) .. GENERATED FROM PYTHON SOURCE LINES 83-89 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 89-105 .. code-block:: Python 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 [-1.68351474 -1.68463982 -2.27655683 -1.68930875 -3.40608478] [[-1.6835146] [-1.6846399] [-2.276558 ] [-1.6893082] [-3.4060864]] .. GENERATED FROM PYTHON SOURCE LINES 106-107 Display the ONNX graph. .. GENERATED FROM PYTHON SOURCE LINES 107-126 .. code-block:: Python 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 (np.float64(-0.5), np.float64(4921.5), np.float64(13264.5), np.float64(-0.5)) .. GENERATED FROM PYTHON SOURCE LINES 127-129 Processing time +++++++++++++++ .. GENERATED FROM PYTHON SOURCE LINES 129-144 .. code-block:: Python 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 0.19538224400093895 0.236580953001976 .. GENERATED FROM PYTHON SOURCE LINES 145-146 The model using ReduceLogSumExp is much faster. .. GENERATED FROM PYTHON SOURCE LINES 148-155 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 155-168 .. code-block:: Python 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 169-170 **Versions used for this example** .. GENERATED FROM PYTHON SOURCE LINES 170-180 .. code-block:: Python import sklearn print("numpy:", numpy.__version__) print("scikit-learn:", sklearn.__version__) import skl2onnx print("onnx: ", onnx.__version__) print("onnxruntime: ", onnxruntime.__version__) print("skl2onnx: ", skl2onnx.__version__) .. rst-class:: sphx-glr-script-out .. code-block:: none numpy: 2.2.0 scikit-learn: 1.6.0 onnx: 1.18.0 onnxruntime: 1.21.0+cu126 skl2onnx: 1.18.0 .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 12.799 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-jupyter :download:`Download Jupyter notebook: plot_black_op.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_black_op.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_black_op.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_