Unexpected behavior for float64 with InferenceSession sklearn

[maulberto3]

The following code won't work, onnx will complain that is expecting float32 when everything was float64: data, model and convert [ONNXRuntimeError] : 1 : FAIL : Load model from rf_iris.onnx failed:Type Error: Type (tensor(double)) of output arg (variable) of node (TreeEnsembleRegressor) does not match expected type (tensor(float)). .

Is this ok? Or maybe I am missing something?

from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor

from skl2onnx import convert_sklearn, to_onnx
from skl2onnx.common.data_types import DoubleTensorType

import onnxruntime as rt


# Train
X, y = make_regression(100, 10, bias=1, noise=1, random_state=16)  # float64
X.dtype, y.dtype
X_train, X_test, y_train, y_test = train_test_split(X, y)
rf = RandomForestRegressor(n_jobs=-1, random_state=16)
_ = rf.fit(X_train, y_train)
rf.predict(X_test).dtype  # float64

# Convert
init_types = [('double_input', DoubleTensorType([None, 10]))]
onx = convert_sklearn(rf, initial_types=init_types)
with open("rf_iris.onnx", "wb") as f:
    _ = f.write(onx.SerializeToString())

# Call
sess = rt.InferenceSession("rf_iris.onnx", )
input_name = sess.get_inputs()[0].name
label_name = sess.get_outputs()[0].name
sess.run(None, {input_name: X_test})[0]
sess.run([label_name], {input_name: X_test})[0]

[xadupre]

Some versions of onnxruntime only supports floats for TreeEnsemble. Which version are you using? If it is the latest, 1.17.1, this should have been fixed. But the maybe the converting library was not updated to support it. I'll check.