注意
转到末尾 以下载完整的示例代码
日志记录，详细¶
如果管道包含没有关联转换器的对象，则管道转换将失败。如果其中一个对象由自定义转换器映射，则它也可能失败。如果错误消息不够明确，则可以启用日志记录。
训练模型¶
使用随机森林和鸢尾花数据集的非常基本的示例。
import logging
import numpy
import onnx
import onnxruntime as rt
import sklearn
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from skl2onnx.common.data_types import FloatTensorType
from skl2onnx import convert_sklearn
import skl2onnx
iris = load_iris()
X, y = iris.data, iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y)
clr = DecisionTreeClassifier()
clr.fit(X_train, y_train)
print(clr)
DecisionTreeClassifier()
将模型转换为 ONNX¶
initial_type = [("float_input", FloatTensorType([None, 4]))]
onx = convert_sklearn(clr, initial_types=initial_type, target_opset=12)
sess = rt.InferenceSession(onx.SerializeToString(), providers=["CPUExecutionProvider"])
input_name = sess.get_inputs()[0].name
label_name = sess.get_outputs()[0].name
pred_onx = sess.run([label_name], {input_name: X_test.astype(numpy.float32)})[0]
print(pred_onx)
[2 0 0 1 0 0 0 1 1 0 2 0 0 1 2 1 0 0 0 2 1 0 0 1 2 2 2 1 2 0 2 1 1 2 0 0 0
0]
使用 verbose 参数转换¶
verbose 是一个在标准输出上打印消息的参数。它告诉调用了哪个转换器。verbose=1 通常意味着 skl2onnx 在转换管道时正在做什么。 verbose=2+ 保留用于转换器内部的信息。
convert_sklearn(clr, initial_types=initial_type, target_opset=12, verbose=1)
[convert_sklearn] parse_sklearn_model
[convert_sklearn] convert_topology
[convert_operators] begin
[convert_operators] iteration 1 - n_vars=0 n_ops=2
[call_converter] call converter for 'SklearnDecisionTreeClassifier'.
[call_converter] call converter for 'SklearnZipMap'.
[convert_operators] end iter: 1 - n_vars=5
[convert_operators] iteration 2 - n_vars=5 n_ops=2
[convert_operators] end iter: 2 - n_vars=5
[convert_operators] end.
[_update_domain_version] +opset 0: name='', version=9
[_update_domain_version] +opset 1: name='ai.onnx.ml', version=1
[convert_sklearn] end
ir_version: 7
opset_import {
domain: ""
version: 9
}
opset_import {
domain: "ai.onnx.ml"
version: 1
}
producer_name: "skl2onnx"
producer_version: "1.16.0"
domain: "ai.onnx"
model_version: 0
doc_string: ""
graph {
node {
input: "float_input"
output: "label"
output: "probabilities"
name: "TreeEnsembleClassifier"
op_type: "TreeEnsembleClassifier"
domain: "ai.onnx.ml"
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name: "class_ids"
type: INTS
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使用日志记录转换¶
这是非常详细的日志记录。它会处理哪些运算符或变量（转换器的输出），创建了哪些节点……当实现自定义转换器时，此信息可能很有用。
logger = logging.getLogger("skl2onnx")
logger.setLevel(logging.DEBUG)
convert_sklearn(clr, initial_types=initial_type, target_opset=12)
ir_version: 7
opset_import {
domain: ""
version: 9
}
opset_import {
domain: "ai.onnx.ml"
version: 1
}
producer_name: "skl2onnx"
producer_version: "1.16.0"
domain: "ai.onnx"
model_version: 0
doc_string: ""
graph {
node {
input: "float_input"
output: "label"
output: "probabilities"
name: "TreeEnsembleClassifier"
op_type: "TreeEnsembleClassifier"
domain: "ai.onnx.ml"
attribute {
name: "class_ids"
type: INTS
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domain: ""
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domain: "ai.onnx.ml"
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input {
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output {
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output {
name: "output_probability"
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以及如何禁用它。
logger.setLevel(logging.INFO)
convert_sklearn(clr, initial_types=initial_type, target_opset=12)
logger.setLevel(logging.WARNING)
用于此示例的版本
print("numpy:", numpy.__version__)
print("scikit-learn:", sklearn.__version__)
print("onnx: ", onnx.__version__)
print("onnxruntime: ", rt.__version__)
print("skl2onnx: ", skl2onnx.__version__)
numpy: 1.23.5
scikit-learn: 1.4.dev0
onnx: 1.15.0
onnxruntime: 1.16.0+cu118
skl2onnx: 1.16.0
脚本的总运行时间：（0 分钟 0.063 秒）