mirror of
https://github.com/saymrwulf/onnxruntime.git
synced 2026-07-14 18:12:05 +00:00
e210853b0c
* Update python documentation * remove two unnecessary files Co-authored-by: xavier dupré <xavier.dupre@gmail.com>
546 lines
11 KiB
ReStructuredText
546 lines
11 KiB
ReStructuredText
.. only:: html
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.. note::
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:class: sphx-glr-download-link-note
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Click :ref:`here <sphx_glr_download_auto_examples_plot_train_convert_predict.py>` to download the full example code
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.. rst-class:: sphx-glr-example-title
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.. _sphx_glr_auto_examples_plot_train_convert_predict.py:
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.. _l-logreg-example:
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Train, convert and predict with ONNX Runtime
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============================================
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This example demonstrates an end to end scenario
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starting with the training of a machine learned model
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to its use in its converted from.
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.. contents::
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:local:
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Train a logistic regression
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+++++++++++++++++++++++++++
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The first step consists in retrieving the iris datset.
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.. code-block:: default
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from sklearn.datasets import load_iris
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iris = load_iris()
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X, y = iris.data, iris.target
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from sklearn.model_selection import train_test_split
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X_train, X_test, y_train, y_test = train_test_split(X, y)
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Then we fit a model.
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.. code-block:: default
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from sklearn.linear_model import LogisticRegression
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clr = LogisticRegression()
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clr.fit(X_train, y_train)
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.. rst-class:: sphx-glr-script-out
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Out:
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.. code-block:: none
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LogisticRegression()
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We compute the prediction on the test set
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and we show the confusion matrix.
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.. code-block:: default
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from sklearn.metrics import confusion_matrix
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pred = clr.predict(X_test)
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print(confusion_matrix(y_test, pred))
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.. rst-class:: sphx-glr-script-out
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Out:
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.. code-block:: none
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[[15 0 0]
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[ 0 11 1]
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[ 0 2 9]]
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Conversion to ONNX format
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+++++++++++++++++++++++++
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We use module
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`sklearn-onnx <https://github.com/onnx/sklearn-onnx>`_
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to convert the model into ONNX format.
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.. code-block:: default
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from skl2onnx import convert_sklearn
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from skl2onnx.common.data_types import FloatTensorType
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initial_type = [('float_input', FloatTensorType([None, 4]))]
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onx = convert_sklearn(clr, initial_types=initial_type)
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with open("logreg_iris.onnx", "wb") as f:
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f.write(onx.SerializeToString())
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We load the model with ONNX Runtime and look at
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its input and output.
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.. code-block:: default
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import onnxruntime as rt
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sess = rt.InferenceSession("logreg_iris.onnx")
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print("input name='{}' and shape={}".format(
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sess.get_inputs()[0].name, sess.get_inputs()[0].shape))
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print("output name='{}' and shape={}".format(
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sess.get_outputs()[0].name, sess.get_outputs()[0].shape))
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.. rst-class:: sphx-glr-script-out
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Out:
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.. code-block:: none
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input name='float_input' and shape=[None, 4]
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output name='output_label' and shape=[None]
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We compute the predictions.
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.. code-block:: default
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input_name = sess.get_inputs()[0].name
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label_name = sess.get_outputs()[0].name
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import numpy
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pred_onx = sess.run([label_name], {input_name: X_test.astype(numpy.float32)})[0]
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print(confusion_matrix(pred, pred_onx))
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.. rst-class:: sphx-glr-script-out
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Out:
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.. code-block:: none
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[[15 0 0]
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[ 0 13 0]
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[ 0 0 10]]
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The prediction are perfectly identical.
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Probabilities
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+++++++++++++
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Probabilities are needed to compute other
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relevant metrics such as the ROC Curve.
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Let's see how to get them first with
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scikit-learn.
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.. code-block:: default
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prob_sklearn = clr.predict_proba(X_test)
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print(prob_sklearn[:3])
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.. rst-class:: sphx-glr-script-out
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Out:
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.. code-block:: none
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[[1.73218830e-01 8.23426993e-01 3.35417635e-03]
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[7.42494153e-02 9.21910775e-01 3.83980986e-03]
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[9.74276732e-01 2.57231679e-02 9.98201855e-08]]
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And then with ONNX Runtime.
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The probabilies appear to be
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.. code-block:: default
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prob_name = sess.get_outputs()[1].name
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prob_rt = sess.run([prob_name], {input_name: X_test.astype(numpy.float32)})[0]
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import pprint
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pprint.pprint(prob_rt[0:3])
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.. rst-class:: sphx-glr-script-out
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Out:
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.. code-block:: none
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[{0: 0.17321892082691193, 1: 0.8234269618988037, 2: 0.003354174317792058},
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{0: 0.07424944639205933, 1: 0.9219107627868652, 2: 0.0038398131728172302},
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{0: 0.9742767214775085, 1: 0.025723155587911606, 2: 9.982016280218886e-08}]
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Let's benchmark.
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.. code-block:: default
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from timeit import Timer
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def speed(inst, number=10, repeat=20):
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timer = Timer(inst, globals=globals())
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raw = numpy.array(timer.repeat(repeat, number=number))
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ave = raw.sum() / len(raw) / number
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mi, ma = raw.min() / number, raw.max() / number
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print("Average %1.3g min=%1.3g max=%1.3g" % (ave, mi, ma))
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return ave
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print("Execution time for clr.predict")
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speed("clr.predict(X_test)")
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print("Execution time for ONNX Runtime")
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speed("sess.run([label_name], {input_name: X_test.astype(numpy.float32)})[0]")
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.. rst-class:: sphx-glr-script-out
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Out:
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.. code-block:: none
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Execution time for clr.predict
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Average 7.59e-05 min=4.75e-05 max=0.000155
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Execution time for ONNX Runtime
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Average 3.9e-05 min=3.67e-05 max=6.01e-05
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3.902899999999931e-05
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Let's benchmark a scenario similar to what a webservice
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experiences: the model has to do one prediction at a time
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as opposed to a batch of prediction.
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.. code-block:: default
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def loop(X_test, fct, n=None):
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nrow = X_test.shape[0]
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if n is None:
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n = nrow
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for i in range(0, n):
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im = i % nrow
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fct(X_test[im: im+1])
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print("Execution time for clr.predict")
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speed("loop(X_test, clr.predict, 100)")
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def sess_predict(x):
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return sess.run([label_name], {input_name: x.astype(numpy.float32)})[0]
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print("Execution time for sess_predict")
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speed("loop(X_test, sess_predict, 100)")
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.. rst-class:: sphx-glr-script-out
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Out:
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.. code-block:: none
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Execution time for clr.predict
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Average 0.00598 min=0.005 max=0.00753
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Execution time for sess_predict
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Average 0.00315 min=0.00248 max=0.00516
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0.003146542500000038
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Let's do the same for the probabilities.
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.. code-block:: default
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print("Execution time for predict_proba")
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speed("loop(X_test, clr.predict_proba, 100)")
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def sess_predict_proba(x):
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return sess.run([prob_name], {input_name: x.astype(numpy.float32)})[0]
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print("Execution time for sess_predict_proba")
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speed("loop(X_test, sess_predict_proba, 100)")
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.. rst-class:: sphx-glr-script-out
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Out:
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.. code-block:: none
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Execution time for predict_proba
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Average 0.00812 min=0.00658 max=0.0116
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Execution time for sess_predict_proba
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Average 0.00287 min=0.00238 max=0.00362
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0.0028650640000000395
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This second comparison is better as
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ONNX Runtime, in this experience,
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computes the label and the probabilities
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in every case.
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Benchmark with RandomForest
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+++++++++++++++++++++++++++
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We first train and save a model in ONNX format.
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.. code-block:: default
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from sklearn.ensemble import RandomForestClassifier
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rf = RandomForestClassifier()
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rf.fit(X_train, y_train)
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initial_type = [('float_input', FloatTensorType([1, 4]))]
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onx = convert_sklearn(rf, initial_types=initial_type)
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with open("rf_iris.onnx", "wb") as f:
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f.write(onx.SerializeToString())
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We compare.
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.. code-block:: default
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sess = rt.InferenceSession("rf_iris.onnx")
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def sess_predict_proba_rf(x):
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return sess.run([prob_name], {input_name: x.astype(numpy.float32)})[0]
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print("Execution time for predict_proba")
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speed("loop(X_test, rf.predict_proba, 100)")
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print("Execution time for sess_predict_proba")
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speed("loop(X_test, sess_predict_proba_rf, 100)")
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.. rst-class:: sphx-glr-script-out
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Out:
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.. code-block:: none
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Execution time for predict_proba
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Average 0.594 min=0.506 max=0.727
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Execution time for sess_predict_proba
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Average 0.00329 min=0.00265 max=0.00614
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0.0032889064999999107
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Let's see with different number of trees.
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.. code-block:: default
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measures = []
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for n_trees in range(5, 51, 5):
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print(n_trees)
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rf = RandomForestClassifier(n_estimators=n_trees)
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rf.fit(X_train, y_train)
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initial_type = [('float_input', FloatTensorType([1, 4]))]
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onx = convert_sklearn(rf, initial_types=initial_type)
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with open("rf_iris_%d.onnx" % n_trees, "wb") as f:
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f.write(onx.SerializeToString())
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sess = rt.InferenceSession("rf_iris_%d.onnx" % n_trees)
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def sess_predict_proba_loop(x):
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return sess.run([prob_name], {input_name: x.astype(numpy.float32)})[0]
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tsk = speed("loop(X_test, rf.predict_proba, 100)", number=5, repeat=5)
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trt = speed("loop(X_test, sess_predict_proba_loop, 100)", number=5, repeat=5)
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measures.append({'n_trees': n_trees, 'sklearn': tsk, 'rt': trt})
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from pandas import DataFrame
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df = DataFrame(measures)
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ax = df.plot(x="n_trees", y="sklearn", label="scikit-learn", c="blue", logy=True)
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df.plot(x="n_trees", y="rt", label="onnxruntime",
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ax=ax, c="green", logy=True)
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ax.set_xlabel("Number of trees")
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ax.set_ylabel("Prediction time (s)")
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ax.set_title("Speed comparison between scikit-learn and ONNX Runtime\nFor a random forest on Iris dataset")
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ax.legend()
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.. image:: /auto_examples/images/sphx_glr_plot_train_convert_predict_001.png
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:alt: Speed comparison between scikit-learn and ONNX Runtime For a random forest on Iris dataset
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:class: sphx-glr-single-img
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.. rst-class:: sphx-glr-script-out
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Out:
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.. code-block:: none
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5
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Average 0.0719 min=0.0581 max=0.079
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Average 0.00291 min=0.00257 max=0.00326
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10
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Average 0.0919 min=0.0727 max=0.106
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Average 0.00241 min=0.00232 max=0.00248
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Average 0.129 min=0.119 max=0.141
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Average 0.00296 min=0.00266 max=0.00327
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Average 0.148 min=0.126 max=0.17
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Average 0.00345 min=0.00309 max=0.00387
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Average 0.184 min=0.155 max=0.206
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Average 0.00298 min=0.00254 max=0.00355
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Average 0.208 min=0.162 max=0.256
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Average 0.00242 min=0.00238 max=0.00249
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Average 0.268 min=0.242 max=0.325
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Average 0.00326 min=0.00272 max=0.00449
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Average 0.513 min=0.422 max=0.796
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Average 0.00399 min=0.00352 max=0.00489
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45
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Average 0.44 min=0.399 max=0.499
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Average 0.00344 min=0.00336 max=0.00355
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50
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Average 0.503 min=0.447 max=0.584
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Average 0.00342 min=0.00318 max=0.00368
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<matplotlib.legend.Legend object at 0x000001878B902EB8>
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 3 minutes 9.986 seconds)
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.. _sphx_glr_download_auto_examples_plot_train_convert_predict.py:
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.. only :: html
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.. container:: sphx-glr-footer
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:class: sphx-glr-footer-example
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.. container:: sphx-glr-download sphx-glr-download-python
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:download:`Download Python source code: plot_train_convert_predict.py <plot_train_convert_predict.py>`
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.. container:: sphx-glr-download sphx-glr-download-jupyter
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:download:`Download Jupyter notebook: plot_train_convert_predict.ipynb <plot_train_convert_predict.ipynb>`
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.. only:: html
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.. rst-class:: sphx-glr-signature
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`Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
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