onnx_extended.tools.onnx_nodes#

convert_onnx_model#

Upgrades a model to the latest opsets.

Parameters:

onnx_model – proto
opsets – list of opsets to update
recursive – looks into subgraphs
use_as_tensor_attributes – use attributes siffixed with as_tensor for trees
verbose – verbosity
_from_opset – tells which opset a node belongs too, only used when onnx_model is a NodeProto
debug_info – unused

Returns:

new proto

enumerate_onnx_node_types#

onnx_extended.tools.onnx_nodes.enumerate_onnx_node_types(model: str | ModelProto | GraphProto, level: int = 0, shapes: Dict[str, TypeProto] | None = None, external: bool = True) → Iterable[Dict[str, str | float]][source]#

Looks into types for every node in a model.

Parameters:

model – a string or a proto
level – level (recursivity level)
shapes – known shapes, returned by :func:onnx.shape_inference.infer_shapes`
external – loads the external data if the model is loaded

Returns:

a list of dictionary which can be turned into a dataframe.

get_hidden_inputs#

onnx_extended.tools.onnx_nodes.get_hidden_inputs(nodes: Iterable[NodeProto]) → Set[str][source]#

Returns the list of hidden inputs used by subgraphs.

Parameters:: nodes – list of nodes
Returns:: list of names

multiply_tree#

onnx_extended.tools.onnx_nodes.multiply_tree(node: NodeProto, n: int, random: bool = True) → NodeProto[source]#

Multiplies the number of trees in TreeEnsemble operator. It replicates the existing trees but permutes features ids and node values if random is True.

Parameters:

node – tree ensemble operator
n – number of times the existing trees must be multiplied
random – permutation or thresholds

Returns:

the new trees

onnx_merge_models#

onnx_extended.tools.onnx_nodes.onnx_merge_models(m1: ModelProto, m2: ModelProto, io_map: List[Tuple[str, str]], verbose: int = 0) → ModelProto[source]#

Merges two models. The functions also checks that the model have the same defined opsets (except for function). If not, the most recent opset is selected.

Parameters:

m1 – first model
m2 – second model
io_map – mapping between outputs of the first model and and the input of the second one
verbose – display some information if one of the model was updated

Returns:

new model

onnx_remove_node_unused#

onnx_extended.tools.onnx_nodes.onnx_remove_node_unused(onnx_model, recursive=True, debug_info=None, **options)[source]#

Removes unused nodes of the graph. An unused node is not involved in the output computation.

Parameters:

onnx_model – onnx model
recursive – looks into subgraphs
debug_info – debug information (private)
options – unused

Returns:

new onnx _model

select_model_inputs_outputs#

onnx_extended.tools.onnx_nodes.select_model_inputs_outputs(model: ModelProto, outputs: List[str] | None = None, inputs: List[str] | None = None, infer_shapes: bool = True, overwrite: Dict[str, Any] | None = None, remove_unused: bool = True, verbose: int = 0)[source]#

Takes a model and changes its outputs.

Parameters:

model – ONNX model
inputs – new inputs, same ones if None
outputs – new outputs, same ones if None
infer_shapes – infer inputs and outputs shapes
overwrite – overwrite type and shapes for inputs or outputs, overwrite is a dictionary {‘name’: (numpy dtype, shape)}
remove_unused – remove unused nodes from the graph
verbose – display information while converting

Returns:

modified model

The function removes unneeded nodes.

The following example shows how to change the inputs of model to bypass the first nodes. Shape inferences fails to determine the new inputs type. They need to be overwritten. verbose=1 shows the number of deleted nodes.

import onnx
from onnx_extended.tools.onnx_nodes import select_model_inputs_outputs

onx = onnx.load(path)
onx2 = select_model_inputs_outputs(
    onx, inputs=["a", "b"],
    infer_shapes=True, verbose=1,
    overwrite={'a': (numpy.int32, None), 'b': (numpy.int64, None)})
onnx.save(onx2, path2)