reference

CReferenceEvaluator

class onnx_extended.reference.CReferenceEvaluator(proto: Any, opsets: Dict[str, int] | None = None, functions: List[ReferenceEvaluator | FunctionProto] | None = None, verbose: int = 0, new_ops: List[OpRun] | None = None, **kwargs)

This class replaces the python implementation by C implementation for a short list of operators quite slow in python (such as Conv). The class automatically replaces a python implementation by a C implementation if available. See example Using C implementation of operator Conv.

from onnx.reference import ReferenceEvaluator
from from onnx.reference.c_ops import Conv
ref = ReferenceEvaluator(..., new_ops=[Conv])

property input_names

Returns the input names.

property opsets

Returns the opsets.

property output_names

Returns the output names.

run(output_names, feed_inputs: Dict[str, Any], attributes: Dict[str, Any] | None = None)

Executes the onnx model.

Parameters:

• output_names – requested outputs by names, None for all

• feed_inputs – dictionary { input name: input value }

• attributes – attributes value if the instance runs a FunctionProto

Returns:

list of requested outputs

Operators

ai.onnx

class onnx_extended.reference.c_ops.c_op_conv.Conv(onnx_node: NodeProto, run_params: Dict[str, Any], schema: Any | None = None)

ai.onnx.ml

class onnx_extended.reference.c_ops.c_op_tree_ensemble_classifier.TreeEnsembleClassifier_1(onnx_node: NodeProto, run_params: Dict[str, Any], schema: Any | None = None)

class onnx_extended.reference.c_ops.c_op_tree_ensemble_classifier.TreeEnsembleClassifier_3(onnx_node: NodeProto, run_params: Dict[str, Any], schema: Any | None = None)

class onnx_extended.reference.c_ops.c_op_tree_ensemble_regressor.TreeEnsembleRegressor_1(onnx_node: NodeProto, run_params: Dict[str, Any], schema: Any | None = None)

class onnx_extended.reference.c_ops.c_op_tree_ensemble_regressor.TreeEnsembleRegressor_3(onnx_node: NodeProto, run_params: Dict[str, Any], schema: Any | None = None)