注意
转到结尾 下载完整的示例代码。
什么是 opset 数字？¶
每个库都有版本号。 scikit-learn 可能会更改特定模型的实现。 例如，SVC 模型在 0.22 中添加了参数 break_ties。 ONNX 也有一个称为 opset 数字 的版本。 操作符 ArgMin 在 opset 1 中添加，并在 opset 11、12、13 中更改。 有时，它会更新以扩展其支持的类型列表，有时，它会将参数移到输入列表中。 用于部署模型的运行时没有实现新版本，在这种情况下，模型必须通常使用运行时支持的最新 opset 进行转换，我们称之为 目标 opset。 ONNX 图表只包含一个唯一的 opset，每个节点必须根据目标 opset 以下的最新 opset 中定义的规范进行描述。
此示例考虑了一个 IsolationForest 并深入研究了 opset。
数据¶
一个简单的例子。
from onnx.defs import onnx_opset_version
from skl2onnx import to_onnx
import numpy
import matplotlib.pyplot as plt
from sklearn.ensemble import IsolationForest
from sklearn.datasets import make_blobs
X, y = make_blobs(n_samples=100, n_features=2)
model = IsolationForest(n_estimators=3)
model.fit(X)
labels = model.predict(X)
fig, ax = plt.subplots(1, 1)
for k in (-1, 1):
ax.plot(X[labels == k, 0], X[labels == k, 1], "o", label="cl%d" % k)
ax.set_title("Sample")
ONNX¶
onx = to_onnx(
model, X[:1].astype(numpy.float32), target_opset={"": 15, "ai.onnx.ml": 2}
)
print(onx)
ir_version: 8
opset_import {
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version: 2
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version: 15
}
producer_name: "skl2onnx"
producer_version: "1.17.0"
domain: "ai.onnx"
model_version: 0
doc_string: ""
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name: "eqp2p_m1_2_Max1"
op_type: "Max"
domain: ""
}
node {
input: "eqp_log0_output0"
input: "eqp_log0_Addcst"
output: "eqp_log0_C01"
name: "eqp_log0_Add"
op_type: "Add"
domain: ""
}
node {
input: "eqp2p_m1_1_max02"
input: "eqp_ns1_max0"
output: "eqp_ns1_C01"
name: "eqp_ns1_Div"
op_type: "Div"
domain: ""
}
node {
input: "eqp2p_m1_2_max02"
input: "eqp_ns2_max0"
output: "eqp_ns2_C01"
name: "eqp_ns2_Div"
op_type: "Div"
domain: ""
}
node {
input: "eqp2p_m1_1_max0"
output: "eqp_log1_output0"
name: "eqp_log1_Log"
op_type: "Log"
domain: ""
}
node {
input: "eqp2p_m1_2_max0"
output: "eqp_log2_output0"
name: "eqp_log2_Log"
op_type: "Log"
domain: ""
}
node {
input: "eqp_ns0_C01"
input: "eqp_ns0_Mulcst"
output: "eqp_ns0_C0"
name: "eqp_ns0_Mul"
op_type: "Mul"
domain: ""
}
node {
input: "eqp_log1_output0"
input: "eqp_log0_Addcst"
output: "eqp_log1_C01"
name: "eqp_log1_Add"
op_type: "Add"
domain: ""
}
node {
input: "eqp_log2_output0"
input: "eqp_log0_Addcst"
output: "eqp_log2_C01"
name: "eqp_log2_Add"
op_type: "Add"
domain: ""
}
node {
input: "eqp_log0_C01"
input: "dec_Powcst"
output: "eqp_log0_C0"
name: "eqp_log0_Mul"
op_type: "Mul"
domain: ""
}
node {
input: "eqp_ns1_C01"
input: "eqp_ns0_Mulcst"
output: "eqp_ns1_C0"
name: "eqp_ns1_Mul"
op_type: "Mul"
domain: ""
}
node {
input: "eqp_ns2_C01"
input: "eqp_ns0_Mulcst"
output: "eqp_ns2_C0"
name: "eqp_ns2_Mul"
op_type: "Mul"
domain: ""
}
node {
input: "eqp_log0_C0"
input: "eqp_ns0_C0"
output: "avlog0_C01"
name: "avlog0_Add"
op_type: "Add"
domain: ""
}
node {
input: "eqp_log1_C01"
input: "dec_Powcst"
output: "eqp_log1_C0"
name: "eqp_log1_Mul"
op_type: "Mul"
domain: ""
}
node {
input: "eqp_log2_C01"
input: "dec_Powcst"
output: "eqp_log2_C0"
name: "eqp_log2_Mul"
op_type: "Mul"
domain: ""
}
node {
input: "eqp_log1_C0"
input: "eqp_ns1_C0"
output: "avlog1_C01"
name: "avlog1_Add"
op_type: "Add"
domain: ""
}
node {
input: "eqp_log2_C0"
input: "eqp_ns2_C0"
output: "avlog2_C01"
name: "avlog2_Add"
op_type: "Add"
domain: ""
}
node {
input: "avlog0_C01"
input: "plus2_0_output0"
output: "avlog0_C0"
name: "avlog0_Mul"
op_type: "Mul"
domain: ""
}
node {
input: "eq2_0_output0"
input: "avlog0_C0"
output: "avpl0_C0"
name: "avpl0_Add"
op_type: "Add"
domain: ""
}
node {
input: "avlog1_C01"
input: "plus2_1_output0"
output: "avlog1_C0"
name: "avlog1_Mul"
op_type: "Mul"
domain: ""
}
node {
input: "avlog2_C01"
input: "plus2_2_output0"
output: "avlog2_C0"
name: "avlog2_Mul"
op_type: "Mul"
domain: ""
}
node {
input: "eq2_1_output0"
input: "avlog1_C0"
output: "avpl1_C0"
name: "avpl1_Add"
op_type: "Add"
domain: ""
}
node {
input: "eq2_2_output0"
input: "avlog2_C0"
output: "avpl2_C0"
name: "avpl2_Add"
op_type: "Add"
domain: ""
}
node {
input: "path_length0_reshaped0"
input: "avpl0_C0"
output: "depth0_C01"
name: "depth0_Add"
op_type: "Add"
domain: ""
}
node {
input: "depth0_C01"
input: "eqp2p_m1_0_Addcst"
output: "depth0_C0"
name: "depth0_Add1"
op_type: "Add"
domain: ""
}
node {
input: "path_length1_reshaped0"
input: "avpl1_C0"
output: "depth1_C01"
name: "depth1_Add"
op_type: "Add"
domain: ""
}
node {
input: "path_length2_reshaped0"
input: "avpl2_C0"
output: "depth2_C01"
name: "depth2_Add"
op_type: "Add"
domain: ""
}
node {
input: "depth1_C01"
input: "eqp2p_m1_0_Addcst"
output: "depth1_C0"
name: "depth1_Add1"
op_type: "Add"
domain: ""
}
node {
input: "depth2_C01"
input: "eqp2p_m1_0_Addcst"
output: "depth2_C0"
name: "depth2_Add1"
op_type: "Add"
domain: ""
}
node {
input: "depth0_C0"
input: "depth1_C0"
input: "depth2_C0"
output: "dec_sum0"
name: "dec_Sum"
op_type: "Sum"
domain: ""
}
node {
input: "dec_sum0"
input: "dec_Divcst"
output: "dec_C0"
name: "dec_Div"
op_type: "Div"
domain: ""
}
node {
input: "dec_C0"
output: "dec_Y01"
name: "dec_Neg"
op_type: "Neg"
domain: ""
}
node {
input: "dec_Powcst"
input: "dec_Y01"
output: "dec_Z0"
name: "dec_Pow"
op_type: "Pow"
domain: ""
}
node {
input: "dec_Z0"
output: "dec_Y0"
name: "dec_Neg1"
op_type: "Neg"
domain: ""
}
node {
input: "dec_Y0"
input: "dec_Addcst"
output: "scores"
name: "dec_Add"
op_type: "Add"
domain: ""
}
node {
input: "scores"
input: "eqp2p_m1_0_Maxcst1"
output: "predict_C01"
name: "predict_Less"
op_type: "Less"
domain: ""
}
node {
input: "predict_C01"
output: "predict_output0"
name: "predict_Cast"
op_type: "Cast"
domain: ""
attribute {
name: "to"
type: INT
i: 7
}
}
node {
input: "predict_output0"
input: "predict_Mulcst"
output: "predict_C0"
name: "predict_Mul"
op_type: "Mul"
domain: ""
}
node {
input: "predict_C0"
input: "predict_Addcst"
output: "label"
name: "predict_Add"
op_type: "Add"
domain: ""
}
name: "ONNX(IsolationForest)"
initializer {
dims: 1
data_type: 1
float_data: 2
name: "dec_Powcst"
}
initializer {
dims: 2
data_type: 7
int64_data: 0
int64_data: 1
name: "node_sample0_Gathercst"
}
initializer {
dims: 2
data_type: 7
int64_data: -1
int64_data: 1
name: "path_length0_Reshapecst"
}
initializer {
dims: 1
data_type: 1
float_data: -1
name: "eqp2p_m1_0_Addcst"
}
initializer {
dims: 1
data_type: 1
float_data: 1
name: "eqp2p_m1_0_Maxcst"
}
initializer {
dims: 1
data_type: 1
float_data: 0.577215672
name: "eqp_log0_Addcst"
}
initializer {
dims: 1
data_type: 1
float_data: 0
name: "eqp2p_m1_0_Maxcst1"
}
initializer {
dims: 1
data_type: 1
float_data: -2
name: "eqp_ns0_Mulcst"
}
initializer {
dims: 1
dims: 1
data_type: 1
float_data: 25.0940132
name: "dec_Divcst"
}
initializer {
dims: 1
data_type: 1
float_data: 0.5
name: "dec_Addcst"
}
initializer {
dims: 1
data_type: 7
int64_data: -2
name: "predict_Mulcst"
}
initializer {
dims: 1
data_type: 7
int64_data: 1
name: "predict_Addcst"
}
input {
name: "X"
type {
tensor_type {
elem_type: 1
shape {
dim {
}
dim {
dim_value: 2
}
}
}
}
}
output {
name: "label"
type {
tensor_type {
elem_type: 7
shape {
dim {
}
dim {
dim_value: 1
}
}
}
}
}
output {
name: "scores"
type {
tensor_type {
elem_type: 1
shape {
dim {
}
dim {
dim_value: 1
}
}
}
}
}
}
最后一行显示了 opset。 让我们提取它。
domains = onx.opset_import
for dom in domains:
print("domain: %r, version: %r" % (dom.domain, dom.version))
domain: 'ai.onnx.ml', version: 2
domain: '', version: 15
有两个 opset，一个用于标准操作符，另一个用于机器学习操作符。
ONNX 和 opset¶
转换器可以将模型转换为比默认 opset 更旧的 opset，从 1 到最后一个可用 opset。
def get_domain_opset(onx):
domains = onx.opset_import
res = [{"domain": dom.domain, "version": dom.version} for dom in domains]
return {d["domain"]: d["version"] for d in res}
for opset in range(6, onnx_opset_version() + 1):
try:
onx = to_onnx(
model,
X[:1].astype(numpy.float32),
target_opset={"": opset, "ai.onnx.ml": 2},
)
except RuntimeError as e:
print("target: %r error: %r" % (opset, e))
continue
nodes = len(onx.graph.node)
print("target: %r --> %s %d" % (opset, get_domain_opset(onx), nodes))
target: 6 --> {'ai.onnx.ml': 2, '': 6} 91
target: 7 --> {'': 7, 'ai.onnx.ml': 2} 91
target: 8 --> {'': 8, 'ai.onnx.ml': 2} 91
target: 9 --> {'ai.onnx.ml': 2, '': 9} 91
target: 10 --> {'ai.onnx.ml': 2, '': 10} 91
target: 11 --> {'ai.onnx.ml': 2, '': 11} 91
target: 12 --> {'': 12, 'ai.onnx.ml': 2} 91
target: 13 --> {'': 13, 'ai.onnx.ml': 2} 91
target: 14 --> {'': 14, 'ai.onnx.ml': 2} 91
target: 15 --> {'ai.onnx.ml': 2, '': 15} 91
target: 16 --> {'ai.onnx.ml': 2, '': 16} 91
target: 17 --> {'ai.onnx.ml': 2, '': 17} 91
target: 18 --> {'ai.onnx.ml': 2, '': 18} 91
target: 19 --> {'ai.onnx.ml': 2, '': 19} 91
target: 20 --> {'': 20, 'ai.onnx.ml': 2} 91
target: 21 --> {'ai.onnx.ml': 2, '': 21} 91
/home/xadupre/github/sklearn-onnx/skl2onnx/common/_topology.py:1516: UserWarning: Parameter target_opset 22 > 21 is higher than the the latest tested version.
warnings.warn(
target: 22 error: RuntimeError("The model is using version 22 of domain '' not supported yet by this library. You need to specify target_opset={'': 21}.")
它表明模型无法转换为低于 5 的 opset。 操作符 Reshape 在 opset 5 中更改：一个参数变成了一个输入。 转换器不支持 opset < 5，因为运行时通常不支持。
其他 opset¶
前面的示例更改了主域 '' 的 opset，但其他 opset 域也可以更改。
for opset in range(9, onnx_opset_version() + 1):
for opset_ml in range(1, 4):
tops = {"": opset, "ai.onnx.ml": opset_ml}
try:
print("try target_opset:", tops)
onx = to_onnx(model, X[:1].astype(numpy.float32), target_opset=tops)
except RuntimeError as e:
print("target: %r error: %r" % (opset, e))
continue
nodes = len(onx.graph.node)
print("target: %r --> %s %d" % (opset, get_domain_opset(onx), nodes))
try target_opset: {'': 9, 'ai.onnx.ml': 1}
target: 9 error: RuntimeError("This converter requires at least opset 2 for domain 'ai.onnx.ml'.")
try target_opset: {'': 9, 'ai.onnx.ml': 2}
target: 9 --> {'ai.onnx.ml': 2, '': 9} 91
try target_opset: {'': 9, 'ai.onnx.ml': 3}
target: 9 --> {'ai.onnx.ml': 3, '': 9} 91
try target_opset: {'': 10, 'ai.onnx.ml': 1}
target: 10 error: RuntimeError("This converter requires at least opset 2 for domain 'ai.onnx.ml'.")
try target_opset: {'': 10, 'ai.onnx.ml': 2}
target: 10 --> {'ai.onnx.ml': 2, '': 10} 91
try target_opset: {'': 10, 'ai.onnx.ml': 3}
target: 10 --> {'ai.onnx.ml': 3, '': 10} 91
try target_opset: {'': 11, 'ai.onnx.ml': 1}
target: 11 error: RuntimeError("This converter requires at least opset 2 for domain 'ai.onnx.ml'.")
try target_opset: {'': 11, 'ai.onnx.ml': 2}
target: 11 --> {'ai.onnx.ml': 2, '': 11} 91
try target_opset: {'': 11, 'ai.onnx.ml': 3}
target: 11 --> {'ai.onnx.ml': 3, '': 11} 91
try target_opset: {'': 12, 'ai.onnx.ml': 1}
target: 12 error: RuntimeError("This converter requires at least opset 2 for domain 'ai.onnx.ml'.")
try target_opset: {'': 12, 'ai.onnx.ml': 2}
target: 12 --> {'': 12, 'ai.onnx.ml': 2} 91
try target_opset: {'': 12, 'ai.onnx.ml': 3}
target: 12 --> {'': 12, 'ai.onnx.ml': 3} 91
try target_opset: {'': 13, 'ai.onnx.ml': 1}
target: 13 error: RuntimeError("This converter requires at least opset 2 for domain 'ai.onnx.ml'.")
try target_opset: {'': 13, 'ai.onnx.ml': 2}
target: 13 --> {'': 13, 'ai.onnx.ml': 2} 91
try target_opset: {'': 13, 'ai.onnx.ml': 3}
target: 13 --> {'': 13, 'ai.onnx.ml': 3} 91
try target_opset: {'': 14, 'ai.onnx.ml': 1}
target: 14 error: RuntimeError("This converter requires at least opset 2 for domain 'ai.onnx.ml'.")
try target_opset: {'': 14, 'ai.onnx.ml': 2}
target: 14 --> {'': 14, 'ai.onnx.ml': 2} 91
try target_opset: {'': 14, 'ai.onnx.ml': 3}
target: 14 --> {'': 14, 'ai.onnx.ml': 3} 91
try target_opset: {'': 15, 'ai.onnx.ml': 1}
target: 15 error: RuntimeError("This converter requires at least opset 2 for domain 'ai.onnx.ml'.")
try target_opset: {'': 15, 'ai.onnx.ml': 2}
target: 15 --> {'ai.onnx.ml': 2, '': 15} 91
try target_opset: {'': 15, 'ai.onnx.ml': 3}
target: 15 --> {'': 15, 'ai.onnx.ml': 3} 91
try target_opset: {'': 16, 'ai.onnx.ml': 1}
target: 16 error: RuntimeError("This converter requires at least opset 2 for domain 'ai.onnx.ml'.")
try target_opset: {'': 16, 'ai.onnx.ml': 2}
target: 16 --> {'ai.onnx.ml': 2, '': 16} 91
try target_opset: {'': 16, 'ai.onnx.ml': 3}
target: 16 --> {'': 16, 'ai.onnx.ml': 3} 91
try target_opset: {'': 17, 'ai.onnx.ml': 1}
target: 17 error: RuntimeError("This converter requires at least opset 2 for domain 'ai.onnx.ml'.")
try target_opset: {'': 17, 'ai.onnx.ml': 2}
target: 17 --> {'ai.onnx.ml': 2, '': 17} 91
try target_opset: {'': 17, 'ai.onnx.ml': 3}
target: 17 --> {'': 17, 'ai.onnx.ml': 3} 91
try target_opset: {'': 18, 'ai.onnx.ml': 1}
target: 18 error: RuntimeError("This converter requires at least opset 2 for domain 'ai.onnx.ml'.")
try target_opset: {'': 18, 'ai.onnx.ml': 2}
target: 18 --> {'ai.onnx.ml': 2, '': 18} 91
try target_opset: {'': 18, 'ai.onnx.ml': 3}
target: 18 --> {'': 18, 'ai.onnx.ml': 3} 91
try target_opset: {'': 19, 'ai.onnx.ml': 1}
target: 19 error: RuntimeError("This converter requires at least opset 2 for domain 'ai.onnx.ml'.")
try target_opset: {'': 19, 'ai.onnx.ml': 2}
target: 19 --> {'ai.onnx.ml': 2, '': 19} 91
try target_opset: {'': 19, 'ai.onnx.ml': 3}
target: 19 --> {'': 19, 'ai.onnx.ml': 3} 91
try target_opset: {'': 20, 'ai.onnx.ml': 1}
target: 20 error: RuntimeError("This converter requires at least opset 2 for domain 'ai.onnx.ml'.")
try target_opset: {'': 20, 'ai.onnx.ml': 2}
target: 20 --> {'': 20, 'ai.onnx.ml': 2} 91
try target_opset: {'': 20, 'ai.onnx.ml': 3}
target: 20 --> {'': 20, 'ai.onnx.ml': 3} 91
try target_opset: {'': 21, 'ai.onnx.ml': 1}
target: 21 error: RuntimeError("This converter requires at least opset 2 for domain 'ai.onnx.ml'.")
try target_opset: {'': 21, 'ai.onnx.ml': 2}
target: 21 --> {'ai.onnx.ml': 2, '': 21} 91
try target_opset: {'': 21, 'ai.onnx.ml': 3}
target: 21 --> {'': 21, 'ai.onnx.ml': 3} 91
try target_opset: {'': 22, 'ai.onnx.ml': 1}
/home/xadupre/github/sklearn-onnx/skl2onnx/common/_topology.py:1516: UserWarning: Parameter target_opset 22 > 21 is higher than the the latest tested version.
warnings.warn(
target: 22 error: RuntimeError("This converter requires at least opset 2 for domain 'ai.onnx.ml'.")
try target_opset: {'': 22, 'ai.onnx.ml': 2}
target: 22 error: RuntimeError("The model is using version 22 of domain '' not supported yet by this library. You need to specify target_opset={'': 21}.")
try target_opset: {'': 22, 'ai.onnx.ml': 3}
/home/xadupre/github/sklearn-onnx/skl2onnx/common/_topology.py:1516: UserWarning: Parameter target_opset 22 > 21 is higher than the the latest tested version.
warnings.warn(
target: 22 error: RuntimeError("The model is using version 22 of domain '' not supported yet by this library. You need to specify target_opset={'': 21}.")
脚本总运行时间：（0 分钟 1.416 秒）