Static Methods#

Summary #

staticmethod	class parent	truncated documentation
`ParseFromString`	_CustomSchema	Parses this class from a json string.
`__class_getitem__`	NDArray	Overwrites this method.
`__class_getitem__`	OnnxExisting	Enables expression cls[opset]. It returns the appropriate class cls_opset. Parameter op_version should be …
`__class_getitem__`	OnnxOperator	Enables expression cls[opset]. It returns the appropriate class cls_opset. Parameter op_version should be …
`__class_getitem__`	OnnxOperatorFunction	Enables expression cls[opset]. It returns the appropriate class cls_opset. Parameter op_version should be …
`_add_node`	XGBClassifierConverter
`_add_node`	XGBConverter
`_add_node`	XGBRegressorConverter
`_create_model`	GPT2TokenizerTransformer
`_create_model`	SentencePieceTokenizerTransformer
`_fill_node_attributes`	XGBClassifierConverter
`_fill_node_attributes`	XGBConverter
`_fill_node_attributes`	XGBRegressorConverter
`_find_local_inputs`	OnnxInferenceNode	Determines the local inputs. It is any defined input used by the subgraph and defined in the parent graph.
`_find_static_inputs`	OnnxInferenceNode	Determines the loop inputs. It is any defined inputs by the subgraphs + any result used as a constant in …
`_fmatmul00`	FusedMatMul
`_fmatmul01`	FusedMatMul
`_fmatmul10`	FusedMatMul
`_fmatmul11`	FusedMatMul
`_gemm00`	Gemm
`_gemm01`	Gemm
`_gemm10`	Gemm
`_gemm11`	Gemm
`_generate_classes`	WrappedLightGbmBooster
`_get_default_tree_attribute_pairs`	XGBClassifierConverter
`_get_default_tree_attribute_pairs`	XGBConverter
`_get_default_tree_attribute_pairs`	XGBRegressorConverter
`_get_shape`	OnnxShapeInference
`_get_type_property`	OnnxInference
`_load`	OnnxBackendTest
`_make_tuple`	NumpyCode
`_merge_op_version`	OnnxExisting
`_merge_op_version`	OnnxOperator
`_merge_op_version`	OnnxOperatorFunction
`_node_to_graph_get_type`	OnnxExisting
`_node_to_graph_get_type`	OnnxOperator
`_node_to_graph_get_type`	OnnxOperatorFunction
`_node_to_graph_preprocess_list`	OnnxExisting
`_node_to_graph_preprocess_list`	OnnxOperator
`_node_to_graph_preprocess_list`	OnnxOperatorFunction
`_node_to_graph_process_input`	OnnxExisting
`_node_to_graph_process_input`	OnnxOperator
`_node_to_graph_process_input`	OnnxOperatorFunction
`_node_to_graph_reorder_by_name`	OnnxExisting
`_node_to_graph_reorder_by_name`	OnnxOperator
`_node_to_graph_reorder_by_name`	OnnxOperatorFunction
`_norm_L1_inplace`	Normalizer
`_norm_max_inplace`	Normalizer
`_onnx2bigraph_basic`	BiGraph	Implements graph type ‘basic’ for function `onnx2bigraph()`.
`_onnx2bigraph_simplified`	BiGraph	Implements graph type ‘simplified’ for function `onnx2bigraph()`.
`_process_type`	NDArraySameType	Nicknames such as floats, int, ints, all can be used to describe multiple inputs for a signature. …
`_process_type`	NDArraySameTypeSameShape	Nicknames such as floats, int, ints, all can be used to describe multiple inputs for a signature. …
`_process_type`	NDArrayType	Nicknames such as floats, int, ints, all can be used to describe multiple inputs for a signature. …
`_process_type`	NDArrayTypeSameShape	Nicknames such as floats, int, ints, all can be used to describe multiple inputs for a signature. …
`_process_type`	_NDArrayAlias	Nicknames such as floats, int, ints, all can be used to describe multiple inputs for a signature. …
`_read_proto_from_file`	OnnxBackendTest
`_remap_nodeid`	XGBClassifierConverter
`_remap_nodeid`	XGBConverter
`_remap_nodeid`	XGBRegressorConverter
`_sort`	OnnxBackendTest
`_to_onnx`	OnnxSubEstimator	Converts a model into ONNX and inserts it into an ONNX graph.
`_to_onnx_sklearn`	OnnxSubEstimator	Converts a scikit-learn model into ONNX and inserts it into an ONNX graph. The library relies on …
`_transpose`	FusedMatMul
`attribute_to_value`	OnnxOperatorFunction	Converts an attribute into a value using python structures.
`broadcast`	ShapeResult	Broadcasts dimensions for an element wise operator.
`build_einsum`	CachedEinsum	Creates an instance of CachedEinsum.
`build_rev_keys`	ZipMapDictionary
`cache`	AutoAction	Caches the result of a function.
`common_members`	XGBClassifierConverter	common to regresssor and classifier
`common_members`	XGBConverter	common to regresssor and classifier
`common_members`	XGBRegressorConverter	common to regresssor and classifier
`convert`	XGBClassifierConverter	convert method
`convert`	XGBRegressorConverter	converter method
`create_inference_session`	OnnxInferenceBackend	Instantiates an instance of class @see cl OnnxInference. This method should be overwritten to change the runtime …
`create_inference_session`	OnnxInferenceBackendMicro
`create_inference_session`	OnnxInferenceBackendOrt
`create_inference_session`	OnnxInferenceBackendPyC
`create_inference_session`	OnnxInferenceBackendPyEval
`create_inference_session`	OnnxInferenceBackendShape
`enumerate_create`	OnnxTransformer	Creates multiple OnnxTransformer, one for each requested intermediate node. onnx_bytes : bytes …
`fill_tree_attributes`	XGBClassifierConverter	fills tree attributes
`fill_tree_attributes`	XGBConverter	fills tree attributes
`fill_tree_attributes`	XGBRegressorConverter	fills tree attributes
`from_pb`	Variable	Creates a Variable from a protobuf object.
`from_skl2onnx`	Variable	Converts variable from sklearn-onnx into this class.
`from_skl2onnx_tuple`	Variable	Converts variable from sklearn-onnx into this class defined as a tuple.
`get_unique_name`	OnnxExisting	Returns a unique variable name.
`get_xgb_params`	XGBClassifierConverter	Retrieves parameters of a model.
`get_xgb_params`	XGBConverter	Retrieves parameters of a model.
`get_xgb_params`	XGBRegressorConverter	Retrieves parameters of a model.
`guess_type`	MLActionCst	Guesses a type given a value.
`guess_type`	MLActionVar	Guesses a type given a value.
`is_compatible`	OnnxInferenceBackend	Returns whether the model is compatible with the backend.
`is_compatible`	OnnxInferenceBackendMicro	Returns whether the model is compatible with the backend.
`is_compatible`	OnnxInferenceBackendOrt	Returns whether the model is compatible with the backend.
`is_compatible`	OnnxInferenceBackendPyC	Returns whether the model is compatible with the backend.
`is_compatible`	OnnxInferenceBackendPyEval	Returns whether the model is compatible with the backend.
`is_compatible`	OnnxInferenceBackendShape	Returns whether the model is compatible with the backend.
`is_opset_supported`	OnnxInferenceBackend	Returns whether the opset for the model is supported by the backend.
`is_opset_supported`	OnnxInferenceBackendMicro	Returns whether the opset for the model is supported by the backend.
`is_opset_supported`	OnnxInferenceBackendOrt	Returns whether the opset for the model is supported by the backend.
`is_opset_supported`	OnnxInferenceBackendPyC	Returns whether the opset for the model is supported by the backend.
`is_opset_supported`	OnnxInferenceBackendPyEval	Returns whether the opset for the model is supported by the backend.
`is_opset_supported`	OnnxInferenceBackendShape	Returns whether the opset for the model is supported by the backend.
`local_inputs`	OpFunction	Returns all varibles not registered as inputs and not produced by an node inside the graph. This inputs are part …
`local_inputs`	OpRun	Returns all varibles not registered as inputs and not produced by an node inside the graph. This inputs are part …
`local_inputs`	OpRunArg	Returns all varibles not registered as inputs and not produced by an node inside the graph. This inputs are part …
`local_inputs`	OpRunBinary	Returns all varibles not registered as inputs and not produced by an node inside the graph. This inputs are part …
`local_inputs`	OpRunBinaryComparison	Returns all varibles not registered as inputs and not produced by an node inside the graph. This inputs are part …
`local_inputs`	OpRunBinaryNum	Returns all varibles not registered as inputs and not produced by an node inside the graph. This inputs are part …
`local_inputs`	OpRunBinaryNumpy	Returns all varibles not registered as inputs and not produced by an node inside the graph. This inputs are part …
`local_inputs`	OpRunClassifierProb	Returns all varibles not registered as inputs and not produced by an node inside the graph. This inputs are part …
`local_inputs`	OpRunCustom	Returns all varibles not registered as inputs and not produced by an node inside the graph. This inputs are part …
`local_inputs`	OpRunReduceNumpy	Returns all varibles not registered as inputs and not produced by an node inside the graph. This inputs are part …
`local_inputs`	OpRunUnary	Returns all varibles not registered as inputs and not produced by an node inside the graph. This inputs are part …
`local_inputs`	OpRunUnaryNum	Returns all varibles not registered as inputs and not produced by an node inside the graph. This inputs are part …
`norm_l1`	Normalizer	L1 normalization
`norm_l2`	Normalizer	L2 normalization
`norm_max`	Normalizer	max normalization
`number2alpha`	_GraphBuilder	Converts a numbers into a string keeping the same alphabetical order.
`onnx_graph_distance`	BiGraph	Computes a distance between two ONNX graphs. They must not be too big otherwise this function might take for ever. …
`prepare`	OnnxInferenceBackend	Loads the model and creates @see cl OnnxInference.
`prepare`	OnnxInferenceBackendMicro	Loads the model and creates @see cl OnnxInference.
`prepare`	OnnxInferenceBackendOrt	Loads the model and creates @see cl OnnxInference.
`prepare`	OnnxInferenceBackendPyC	Loads the model and creates @see cl OnnxInference.
`prepare`	OnnxInferenceBackendPyEval	Loads the model and creates @see cl OnnxInference.
`prepare`	OnnxInferenceBackendShape	Loads the model and creates @see cl OnnxInference.
`print_node`	CodeNodeVisitor	Debugging purpose.
`print_node`	CodeNodeVisitor	Debugging purpose.
`process_profiling`	OnnxWholeSession	Flattens json returned by onnxruntime profiling.
`run_model`	OnnxInferenceBackend	Computes the prediction.
`run_model`	OnnxInferenceBackendMicro	Computes the prediction.
`run_model`	OnnxInferenceBackendOrt	Computes the prediction.
`run_model`	OnnxInferenceBackendPyC	Computes the prediction.
`run_model`	OnnxInferenceBackendPyEval	Computes the prediction.
`run_model`	OnnxInferenceBackendShape	Computes the prediction.
`run_node`	OnnxInferenceBackend	This method is not implemented as it is much more efficient to run a whole model than every node independently.
`run_node`	OnnxInferenceBackendMicro	This method is not implemented as it is much more efficient to run a whole model than every node independently.
`run_node`	OnnxInferenceBackendOrt	This method is not implemented as it is much more efficient to run a whole model than every node independently.
`run_node`	OnnxInferenceBackendPyC	This method is not implemented as it is much more efficient to run a whole model than every node independently.
`run_node`	OnnxInferenceBackendPyEval	This method is not implemented as it is much more efficient to run a whole model than every node independently.
`run_node`	OnnxInferenceBackendShape	This method is not implemented as it is much more efficient to run a whole model than every node independently.
`supports_device`	OnnxInferenceBackend	Checks whether the backend is compiled with particular device support.
`supports_device`	OnnxInferenceBackendMicro	Checks whether the backend is compiled with particular device support.
`supports_device`	OnnxInferenceBackendOrt	Checks whether the backend is compiled with particular device support.
`supports_device`	OnnxInferenceBackendPyC	Checks whether the backend is compiled with particular device support.
`supports_device`	OnnxInferenceBackendPyEval	Checks whether the backend is compiled with particular device support.
`supports_device`	OnnxInferenceBackendShape	Checks whether the backend is compiled with particular device support.
`validate`	XGBClassifierConverter
`validate`	XGBConverter	validates the model
`validate`	XGBRegressorConverter