Note
Click here to download the full example code
What is the opset number?#
Every library is versioned. scikit-learn may change the implementation of a specific model. That happens for example with the SVC model where the parameter break_ties was added in 0.22. ONNX does also have a version called opset number. Operator ArgMin was added in opset 1 and changed in opset 11, 12, 13. Sometimes, it is updated to extend the list of types it supports, sometimes, it moves a parameter into the input list. The runtime used to deploy the model does not implement a new version, in that case, a model must be converted by usually using the most recent opset supported by the runtime, we call that opset the targeted opset. An ONNX graph only contains one unique opset, every node must be described following the specifications defined by the latest opset below the targeted opset.
This example considers an IsolationForest and digs into opsets.
Data#
A simple example.
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 (0, 1):
ax.plot(X[labels == k, 0], X[labels == k, 1], 'o', label="cl%d" % k)
ax.set_title("Sample")
Text(0.5, 1.0, 'Sample')
ONNX#
onx = to_onnx(model, X[:1].astype(numpy.float32))
print(onx)
ir_version: 8
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producer_version: "1.13.1"
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node {
input: "eqp_ns0_C01"
input: "eqp_ns0_Mulcst"
output: "eqp_ns0_C0"
name: "eqp_ns0_Mul"
op_type: "Mul"
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_max0"
output: "eqp_log1_output0"
name: "eqp_log1_Log"
op_type: "Log"
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_max0"
output: "eqp_log2_output0"
name: "eqp_log2_Log"
op_type: "Log"
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: "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_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_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: "avlog0_C01"
input: "plus2_0_output0"
output: "avlog0_C0"
name: "avlog0_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: "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: "path_length0_reshaped0"
input: "avpl0_C0"
output: "depth0_C01"
name: "depth0_Add"
op_type: "Add"
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: "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"
attribute {
name: "to"
i: 7
type: INT
}
domain: ""
}
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: 2.0
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: -1.0
name: "eqp2p_m1_0_Addcst"
}
initializer {
dims: 1
data_type: 1
float_data: 1.0
name: "eqp2p_m1_0_Maxcst"
}
initializer {
dims: 1
data_type: 1
float_data: 0.5772156715393066
name: "eqp_log0_Addcst"
}
initializer {
dims: 1
data_type: 1
float_data: 0.0
name: "eqp2p_m1_0_Maxcst1"
}
initializer {
dims: 1
data_type: 1
float_data: -2.0
name: "eqp_ns0_Mulcst"
}
initializer {
dims: 1
dims: 1
data_type: 1
float_data: 25.094013214111328
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
}
}
}
}
}
}
opset_import {
domain: "ai.onnx.ml"
version: 2
}
opset_import {
domain: ""
version: 17
}
The last line shows the opsets. Let’s extract it.
domains = onx.opset_import
for dom in domains:
print(f"domain: {dom.domain!r}, version: {dom.version!r}")
domain: 'ai.onnx.ml', version: 2
domain: '', version: 17
There are two opsets, one for standard operators, the other for machine learning operators.
ONNX and opset#
The converter can convert a model to an older opset than the default one, from 1 to the last available one.
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(1, onnx_opset_version() + 1):
try:
onx = to_onnx(model, X[:1].astype(numpy.float32), target_opset=opset)
except RuntimeError as e:
print(f'target: {opset!r} error: {e!r}')
continue
nodes = len(onx.graph.node)
print('target: %r --> %s %d' % (opset, get_domain_opset(onx), nodes))
target: 1 error: RuntimeError("Operator 'OnnxReshape_5': requested version 1 < 5 schema version.")
target: 2 error: RuntimeError("Operator 'OnnxReshape_5': requested version 2 < 5 schema version.")
target: 3 error: RuntimeError("Operator 'OnnxReshape_5': requested version 3 < 5 schema version.")
target: 4 error: RuntimeError("Operator 'OnnxReshape_5': requested version 4 < 5 schema version.")
target: 5 --> {'ai.onnx.ml': 2, '': 5} 91
target: 6 --> {'ai.onnx.ml': 2, '': 6} 91
target: 7 --> {'': 7, 'ai.onnx.ml': 2} 91
target: 8 --> {'ai.onnx.ml': 2, '': 8} 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
somewhere/workspace/onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/skl2onnx/common/_topology.py:1405: UserWarning: Parameter target_opset 18 > 17 is higher than the the latest tested version.
warnings.warn(
target: 18 error: RuntimeError("The model is using version 18 of domain '' not supported yet by this library. You need to specify target_opset={'': 17}.")
It shows that the model cannot be converted for opset below 5. Operator Reshape changed in opset 5: a parameter became an input. The converter does not support opset < 5 because runtimes usually do not.
Other opsets#
The previous example changed the opset of the main domain
'' but the other opset domain can be changed as well.
for opset in range(9, onnx_opset_version() + 1):
for opset_ml in range(1, 3):
tops = {'': opset, 'ai.onnx.ml': opset_ml}
try:
onx = to_onnx(
model, X[:1].astype(numpy.float32), target_opset=tops)
except RuntimeError as e:
print(f'target: {opset!r} error: {e!r}')
continue
nodes = len(onx.graph.node)
print('target: %r --> %s %d' % (opset, get_domain_opset(onx), nodes))
target: 9 error: RuntimeError("This converter requires at least opset 2 for domain 'ai.onnx.ml'.")
target: 9 --> {'ai.onnx.ml': 2, '': 9} 91
target: 10 error: RuntimeError("This converter requires at least opset 2 for domain 'ai.onnx.ml'.")
target: 10 --> {'ai.onnx.ml': 2, '': 10} 91
target: 11 error: RuntimeError("This converter requires at least opset 2 for domain 'ai.onnx.ml'.")
target: 11 --> {'ai.onnx.ml': 2, '': 11} 91
target: 12 error: RuntimeError("This converter requires at least opset 2 for domain 'ai.onnx.ml'.")
target: 12 --> {'': 12, 'ai.onnx.ml': 2} 91
target: 13 error: RuntimeError("This converter requires at least opset 2 for domain 'ai.onnx.ml'.")
target: 13 --> {'': 13, 'ai.onnx.ml': 2} 91
target: 14 error: RuntimeError("This converter requires at least opset 2 for domain 'ai.onnx.ml'.")
target: 14 --> {'': 14, 'ai.onnx.ml': 2} 91
target: 15 error: RuntimeError("This converter requires at least opset 2 for domain 'ai.onnx.ml'.")
target: 15 --> {'ai.onnx.ml': 2, '': 15} 91
target: 16 error: RuntimeError("This converter requires at least opset 2 for domain 'ai.onnx.ml'.")
target: 16 --> {'ai.onnx.ml': 2, '': 16} 91
target: 17 error: RuntimeError("This converter requires at least opset 2 for domain 'ai.onnx.ml'.")
target: 17 --> {'ai.onnx.ml': 2, '': 17} 91
somewhere/workspace/onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/skl2onnx/common/_topology.py:1405: UserWarning: Parameter target_opset 18 > 17 is higher than the the latest tested version.
warnings.warn(
target: 18 error: RuntimeError("This converter requires at least opset 2 for domain 'ai.onnx.ml'.")
target: 18 error: RuntimeError("The model is using version 18 of domain '' not supported yet by this library. You need to specify target_opset={'': 17}.")
Total running time of the script: ( 0 minutes 18.966 seconds)