.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_tutorial/plot_gconverting.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_tutorial_plot_gconverting.py: Modify the ONNX graph ===================== This example shows how to change the default ONNX graph such as renaming the inputs or outputs names. Basic example +++++++++++++ .. GENERATED FROM PYTHON SOURCE LINES 14-40 .. code-block:: default import numpy from onnxruntime import InferenceSession from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from skl2onnx.common.data_types import FloatTensorType, Int64TensorType from skl2onnx import to_onnx iris = load_iris() X, y = iris.data, iris.target X = X.astype(numpy.float32) X_train, X_test, y_train, y_test = train_test_split(X, y) clr = LogisticRegression(solver="liblinear") clr.fit(X_train, y_train) onx = to_onnx(clr, X, options={"zipmap": False}, target_opset=15) sess = InferenceSession(onx.SerializeToString(), providers=["CPUExecutionProvider"]) input_names = [i.name for i in sess.get_inputs()] output_names = [o.name for o in sess.get_outputs()] print("inputs=%r, outputs=%r" % (input_names, output_names)) print(sess.run(None, {input_names[0]: X_test[:2]})) .. rst-class:: sphx-glr-script-out .. code-block:: none inputs=['X'], outputs=['label', 'probabilities'] [array([1, 1], dtype=int64), array([[0.05684776, 0.8244833 , 0.11866891], [0.05001822, 0.73737246, 0.21260935]], dtype=float32)] .. GENERATED FROM PYTHON SOURCE LINES 41-47 Changes the input names +++++++++++++++++++++++ It is possible to change the input name by using the parameter *initial_types*. However, the user must specify the input types as well. .. GENERATED FROM PYTHON SOURCE LINES 47-63 .. code-block:: default onx = to_onnx( clr, X, options={"zipmap": False}, initial_types=[("X56", FloatTensorType([None, X.shape[1]]))], target_opset=15, ) sess = InferenceSession(onx.SerializeToString(), providers=["CPUExecutionProvider"]) input_names = [i.name for i in sess.get_inputs()] output_names = [o.name for o in sess.get_outputs()] print("inputs=%r, outputs=%r" % (input_names, output_names)) print(sess.run(None, {input_names[0]: X_test[:2]})) .. rst-class:: sphx-glr-script-out .. code-block:: none inputs=['X56'], outputs=['label', 'probabilities'] [array([1, 1], dtype=int64), array([[0.05684776, 0.8244833 , 0.11866891], [0.05001822, 0.73737246, 0.21260935]], dtype=float32)] .. GENERATED FROM PYTHON SOURCE LINES 64-69 Changes the output names ++++++++++++++++++++++++ It is possible to change the input name by using the parameter *final_types*. .. GENERATED FROM PYTHON SOURCE LINES 69-84 .. code-block:: default onx = to_onnx( clr, X, options={"zipmap": False}, final_types=[("L", Int64TensorType([None])), ("P", FloatTensorType([None, 3]))], target_opset=15, ) sess = InferenceSession(onx.SerializeToString(), providers=["CPUExecutionProvider"]) input_names = [i.name for i in sess.get_inputs()] output_names = [o.name for o in sess.get_outputs()] print("inputs=%r, outputs=%r" % (input_names, output_names)) print(sess.run(None, {input_names[0]: X_test[:2]})) .. rst-class:: sphx-glr-script-out .. code-block:: none inputs=['X'], outputs=['L', 'P'] [array([1, 1], dtype=int64), array([[0.05684776, 0.8244833 , 0.11866891], [0.05001822, 0.73737246, 0.21260935]], dtype=float32)] .. GENERATED FROM PYTHON SOURCE LINES 85-91 Renaming intermediate results +++++++++++++++++++++++++++++ It is possible to rename intermediate results by using a prefix or by using a function. The result will be post-processed in order to unique names. It does not impact the graph inputs or outputs. .. GENERATED FROM PYTHON SOURCE LINES 91-108 .. code-block:: default def rename_results(proposed_name, existing_names): result = "_" + proposed_name.upper() while result in existing_names: result += "A" print("changed %r into %r." % (proposed_name, result)) return result onx = to_onnx(clr, X, options={"zipmap": False}, naming=rename_results, target_opset=15) sess = InferenceSession(onx.SerializeToString(), providers=["CPUExecutionProvider"]) input_names = [i.name for i in sess.get_inputs()] output_names = [o.name for o in sess.get_outputs()] print("inputs=%r, outputs=%r" % (input_names, output_names)) print(sess.run(None, {input_names[0]: X_test[:2]})) .. rst-class:: sphx-glr-script-out .. code-block:: none changed 'SklearnLinearClassifier' into '_SKLEARNLINEARCLASSIFIER'. changed 'label' into '_LABEL'. changed 'probabilities' into '_PROBABILITIES'. changed 'LinearClassifier' into '_LINEARCLASSIFIER'. changed 'probability_tensor' into '_PROBABILITY_TENSOR'. changed 'Normalizer' into '_NORMALIZER'. inputs=['X'], outputs=['label', 'probabilities'] [array([1, 1], dtype=int64), array([[0.05684776, 0.8244833 , 0.11866891], [0.05001822, 0.73737246, 0.21260935]], dtype=float32)] .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.065 seconds) .. _sphx_glr_download_auto_tutorial_plot_gconverting.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_gconverting.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_gconverting.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_