Errors with onnxruntime

Many mistakes might happen with onnxruntime. This example looks into several common situations in which onnxruntime does not return the model prediction but raises an exception instead. It starts by loading a model (see Train, convert and predict a model). which produces a logistic regression trained on Iris datasets. The model takes a vector of dimension 2 and returns a class among three.

import skl2onnx
import onnx
import sklearn
import onnxruntime as rt
import numpy as np
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression

try:
    from onnxruntime.capi.onnxruntime_pybind11_state import InvalidArgument
except ImportError:
    # onnxruntime <= 0.5
    InvalidArgument = RuntimeError

data = load_iris()
clr = LogisticRegression().fit(data.data[:, :2], data.target)
with open("logreg_iris.onnx", "wb") as f:
    f.write(
        skl2onnx.to_onnx(
            clr, data.data[:, :2].astype(np.float32), target_opset=12
        ).SerializeToString()
    )

example2 = "logreg_iris.onnx"
sess = rt.InferenceSession(example2, providers=["CPUExecutionProvider"])

input_name = sess.get_inputs()[0].name
output_name = sess.get_outputs()[0].name

The first example fails due to bad types. onnxruntime only expects single floats (4 bytes) and cannot handle any other kind of floats.

try:
    x = np.array([[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0]], dtype=np.float64)
    sess.run([output_name], {input_name: x})
except Exception as e:
    print("Unexpected type")
    print("{0}: {1}".format(type(e), e))

Unexpected type
<class 'onnxruntime.capi.onnxruntime_pybind11_state.InvalidArgument'>: [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Unexpected input data type. Actual: (tensor(double)) , expected: (tensor(float))

The model fails to return an output if the name is misspelled.

try:
    x = np.array([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], dtype=np.float32)
    sess.run(["misspelled"], {input_name: x})
except Exception as e:
    print("Misspelled output name")
    print("{0}: {1}".format(type(e), e))

Misspelled output name
<class 'onnxruntime.capi.onnxruntime_pybind11_state.InvalidArgument'>: [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Invalid output name:misspelled

The output name is optional, it can be replaced by None and onnxruntime will then return all the outputs.

x = np.array([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], dtype=np.float32)
res = sess.run(None, {input_name: x})
print("All outputs")
print(res)

All outputs
[array([0, 0, 0], dtype=int64), [{0: 0.9999734163284302, 1: 2.6633866582415067e-05, 2: 5.49552980899648e-09}, {0: 0.9999914169311523, 1: 8.452885595033877e-06, 2: 1.7398932072865136e-07}, {0: 0.9999918341636658, 1: 2.682675813048263e-06, 2: 5.5084319683373906e-06}]]

The same goes if the input name is misspelled.

try:
    x = np.array([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], dtype=np.float32)
    sess.run([output_name], {"misspelled": x})
except Exception as e:
    print("Misspelled input name")
    print("{0}: {1}".format(type(e), e))

Misspelled input name
<class 'ValueError'>: Required inputs (['X']) are missing from input feed (['misspelled']).

onnxruntime does not necessarily fail if the input dimension is a multiple of the expected input dimension.

for x in [
    np.array([1.0, 2.0, 3.0, 4.0], dtype=np.float32),
    np.array([[1.0, 2.0, 3.0, 4.0]], dtype=np.float32),
    np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32),
    np.array([1.0, 2.0, 3.0], dtype=np.float32),
    np.array([[1.0, 2.0, 3.0]], dtype=np.float32),
]:
    try:
        r = sess.run([output_name], {input_name: x})
        print("Shape={0} and predicted labels={1}".format(x.shape, r))
    except (RuntimeError, InvalidArgument) as e:
        print("Shape={0} and error={1}".format(x.shape, e))

for x in [
    np.array([1.0, 2.0, 3.0, 4.0], dtype=np.float32),
    np.array([[1.0, 2.0, 3.0, 4.0]], dtype=np.float32),
    np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32),
    np.array([1.0, 2.0, 3.0], dtype=np.float32),
    np.array([[1.0, 2.0, 3.0]], dtype=np.float32),
]:
    try:
        r = sess.run(None, {input_name: x})
        print("Shape={0} and predicted probabilities={1}".format(x.shape, r[1]))
    except (RuntimeError, InvalidArgument) as e:
        print("Shape={0} and error={1}".format(x.shape, e))

Shape=(4,) and error=[ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Invalid rank for input: X Got: 1 Expected: 2 Please fix either the inputs/outputs or the model.
Shape=(1, 4) and error=[ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Got invalid dimensions for input: X for the following indices
 index: 1 Got: 4 Expected: 2
 Please fix either the inputs/outputs or the model.
Shape=(2, 2) and predicted labels=[array([0, 0], dtype=int64)]
Shape=(3,) and error=[ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Invalid rank for input: X Got: 1 Expected: 2 Please fix either the inputs/outputs or the model.
Shape=(1, 3) and error=[ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Got invalid dimensions for input: X for the following indices
 index: 1 Got: 3 Expected: 2
 Please fix either the inputs/outputs or the model.
Shape=(4,) and error=[ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Invalid rank for input: X Got: 1 Expected: 2 Please fix either the inputs/outputs or the model.
Shape=(1, 4) and error=[ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Got invalid dimensions for input: X for the following indices
 index: 1 Got: 4 Expected: 2
 Please fix either the inputs/outputs or the model.
Shape=(2, 2) and predicted probabilities=[{0: 0.9999734163284302, 1: 2.6633866582415067e-05, 2: 5.49552980899648e-09}, {0: 0.9999914169311523, 1: 8.452885595033877e-06, 2: 1.7398932072865136e-07}]
Shape=(3,) and error=[ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Invalid rank for input: X Got: 1 Expected: 2 Please fix either the inputs/outputs or the model.
Shape=(1, 3) and error=[ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Got invalid dimensions for input: X for the following indices
 index: 1 Got: 3 Expected: 2
 Please fix either the inputs/outputs or the model.

It does not fail either if the number of dimension is higher than expects but produces a warning.

for x in [
    np.array([[[1.0, 2.0], [3.0, 4.0]]], dtype=np.float32),
    np.array([[[1.0, 2.0, 3.0]]], dtype=np.float32),
    np.array([[[1.0, 2.0]], [[3.0, 4.0]]], dtype=np.float32),
]:
    try:
        r = sess.run([output_name], {input_name: x})
        print("Shape={0} and predicted labels={1}".format(x.shape, r))
    except (RuntimeError, InvalidArgument) as e:
        print("Shape={0} and error={1}".format(x.shape, e))

Shape=(1, 2, 2) and error=[ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Invalid rank for input: X Got: 3 Expected: 2 Please fix either the inputs/outputs or the model.
Shape=(1, 1, 3) and error=[ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Invalid rank for input: X Got: 3 Expected: 2 Please fix either the inputs/outputs or the model.
Shape=(2, 1, 2) and error=[ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Invalid rank for input: X Got: 3 Expected: 2 Please fix either the inputs/outputs or the model.

Versions used for this example

print("numpy:", np.__version__)
print("scikit-learn:", sklearn.__version__)
print("onnx: ", onnx.__version__)
print("onnxruntime: ", rt.__version__)
print("skl2onnx: ", skl2onnx.__version__)

numpy: 2.2.0
scikit-learn: 1.6.0
onnx:  1.18.0
onnxruntime:  1.21.0+cu126
skl2onnx:  1.18.0