Static Methods

Summary

staticmethod	class parent	truncated documentation
__class_getitem__	NDArray	Overwrites this method.
__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_static_inputs	OnnxInferenceNode	Determines the loop inputs. It is any defined inputs by the subgraphs + any results 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
_infer_merged_type	ShapeObject
_infer_merged_type	ShapeObjectFct
_load	OnnxBackendTest
_make_tuple	NumpyCode
_merge_op_version	OnnxOperator
_merge_op_version	OnnxOperatorFunction
_node_to_graph_get_type	OnnxOperator
_node_to_graph_get_type	OnnxOperatorFunction
_node_to_graph_preprocess_list	OnnxOperator
_node_to_graph_preprocess_list	OnnxOperatorFunction
_node_to_graph_process_input	OnnxOperator
_node_to_graph_process_input	OnnxOperatorFunction
_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
_same_	DimensionObject	Returns obj if obj is DimensionObject otherwise converts it.
_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 …
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
einsum_shape	ShapeObject	Computes einsum shapes. Not the most efficient one as it creates variables of the given shapes.
einsum_shape	ShapeObjectFct	Computes einsum shapes. Not the most efficient one as it creates variables of the given shapes.
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 var from sklearn-onnx into this class.
gather_shape	ShapeObject	Computes Gather shapes.
gather_shape	ShapeObjectFct	Computes Gather shapes.
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.
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

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