module `npy.xop`#

Short summary#

module mlprodict.npy.xop

Xop API to build onnx graphs. Inspired from sklearn-onnx.

Classes#

class	truncated documentation
`_CustomSchema`	For operators defined outside onnx.
`_GraphBuilder`	Graph builder. It takes a graph structure made with instances of `OnnxOperatorBase`. The main method is to_onnx. …
`_StaticVariables`	Holds static variables.
`_WrapperLogger`	Wrappers around class `logging.Logger` to take indentation into account.
`_WrapperPrint`	Wrappers around print to help debugging.
`OnnxExisting`	Wrapper around OnnxIdentity to specify this operator is not part of the subgraph it is used in.
`OnnxLoadFactory`	Automatically creating all operators from onnx packages takes time. That’s why function `loadop` only creates …
`OnnxOperator`	Ancestor to every ONNX operator exposed in `mlprodict.npy.xops` and `mlprodict.npy.xops_ml`.
`OnnxOperatorBase`	Base class for `OnnxOperator`, OnnxOperator`Item, :class:`OnnxOperatorTuple.
`OnnxOperatorFunction`	This operator is used to insert existing ONNX function into the ONNX graph being built.
`OnnxOperatorItem`	Accessor to one of the output returned by a `OnnxOperator`.
`OnnxOperatorTuple`	Class used to return multiple `OnnxVar` at the same time.

Functions#

function	truncated documentation
`_default_OPSET_TO_IR_VERSION`	Returns the default mapping between opset and ir_version.
`_domain_to_class_name`	Converts domain into a name.
`_dynamic_class_creation`	Automatically generates classes for each of the operators module onnx defines and described at Operators …
`_find_operator_domain`	Determines the domain of an operator. Raises an exception if not found or if there is an ambiguity.
`_get_all_operator_schema`
`_populate_schemas`	Populates all schemas.
`_split_op_name`
`ClassFactory`	Dynamically creates a class for a specific operator.
`loadop`	Dynamically creates a class for a every operator type in the given list.

Properties#

property	truncated documentation
`all_classes`	Returns all operators wrapped in classes.
`all_domains`	Returns all domains.
`all_schemas`	Returns all schemas.
`all_schemas_versions`	Returns all operators, domains, versions.
`external_inputs`	Returns the list of implicit inputs the subgraph assumes to be existing even if they are not referenced as …
`inputs`	Returns the only inputs in a list.
`inputs`	Returns the only inputs in a list.
`output_names`	Returns self.output_names_.
`output_names`	Returns self.output_names_.
`output_names`	This method should be overwritten.
`output_names`	Returns self.output_names_.
`output_names`	Returns None.
`output_names`	Returns ‘output_names’ of attribute ‘unique’.
`outputs`	Returns ‘output_names’ of attribute ‘unique’.

Static Methods#

staticmethod	truncated documentation
`__class_getitem__`	Enables expression cls[opset]. It returns the appropriate class cls_opset. Parameter op_version should be …
`__class_getitem__`	Enables expression cls[opset]. It returns the appropriate class cls_opset. Parameter op_version should be …
`__class_getitem__`	Enables expression cls[opset]. It returns the appropriate class cls_opset. Parameter op_version should be …
`_merge_op_version`
`_merge_op_version`
`_merge_op_version`
`_node_to_graph_get_type`
`_node_to_graph_get_type`
`_node_to_graph_get_type`
`_node_to_graph_preprocess_list`
`_node_to_graph_preprocess_list`
`_node_to_graph_preprocess_list`
`_node_to_graph_process_input`
`_node_to_graph_process_input`
`_node_to_graph_process_input`
`_node_to_graph_reorder_by_name`
`_node_to_graph_reorder_by_name`
`_node_to_graph_reorder_by_name`
`attribute_to_value`	Converts an attribute into a value using python structures.
`get_unique_name`	Returns a unique variable name.
`number2alpha`	Converts a numbers into a string keeping the same alphabetical order.
`ParseFromString`	Parses this class from a json string.

Methods#

method	truncated documentation
`__abs__`	Automatically adds operator OnnxAbs to the graph.
`__abs__`	Automatically adds operator OnnxAbs to the graph.
`__abs__`	Automatically adds operator OnnxAbs to the graph.
`__add__`	Automatically adds operator OnnxAdd to the graph.
`__add__`	Automatically adds operator OnnxAdd to the graph.
`__add__`	Automatically adds operator OnnxAdd to the graph.
`__call__`	Creates an instance of class `OnnxOperatorFunction`. Equivalent to OnnxOperatorFunction(proto, args, *kwargs). …
`__call__`	Creates an instance of class `OnnxOperatorFunction`. Equivalent to OnnxOperatorFunction(proto, args, *kwargs). …
`__call__`	Creates an instance of class `OnnxOperatorFunction`. Equivalent to OnnxOperatorFunction(proto, args, *kwargs). …
`__eq__`	Automatically adds operator OnnxEqual to the graph.
`__eq__`	Automatically adds operator OnnxEqual to the graph.
`__eq__`	Automatically adds operator OnnxEqual to the graph.
`__eq__`
`__ge__`	Automatically adds operator OnnxGreaterOrEqual to the graph.
`__ge__`	Automatically adds operator OnnxGreaterOrEqual to the graph.
`__ge__`	Automatically adds operator OnnxGreaterOrEqual to the graph.
`__getattr__`	Enables expressions such as:
`__getitem__`	Returns an accessor to one of the output of this node.
`__getitem__`	Returns an accessor to one of the output of this node.
`__getitem__`	Returns an accessor to one of the output of this node.
`__getitem__`	usual
`__gt__`	Automatically adds operator OnnxGreater to the graph.
`__gt__`	Automatically adds operator OnnxGreater to the graph.
`__gt__`	Automatically adds operator OnnxGreater to the graph.
`__init__`
`__init__`
`__init__`
`__init__`
`__init__`
`__init__`
`__init__`
`__init__`
`__init__`
`__init__`
`__init__`	constructor
`__init__`	constructor
`__iter__`	Allows expressions such as `a, b = OnnxTopK(...)`.
`__iter__`	Allows expressions such as `a, b = OnnxTopK(...)`.
`__iter__`	Allows expressions such as `a, b = OnnxTopK(...)`.
`__iter__`	Iterates on the outputs.
`__le__`	Automatically adds operator OnnxLess to the graph.
`__le__`	Automatically adds operator OnnxLess to the graph.
`__le__`	Automatically adds operator OnnxLess to the graph.
`__len__`	usual
`__lt__`	Automatically adds operator OnnxLess to the graph.
`__lt__`	Automatically adds operator OnnxLess to the graph.
`__lt__`	Automatically adds operator OnnxLess to the graph.
`__matmul__`	Automatically adds operator OnnxMatMul to the graph.
`__matmul__`	Automatically adds operator OnnxMatMul to the graph.
`__matmul__`	Automatically adds operator OnnxMatMul to the graph.
`__mod__`	Automatically adds operator OnnxMod to the graph.
`__mod__`	Automatically adds operator OnnxMod to the graph.
`__mod__`	Automatically adds operator OnnxMod to the graph.
`__mul__`	Automatically adds operator OnnxMul to the graph.
`__mul__`	Automatically adds operator OnnxMul to the graph.
`__mul__`	Automatically adds operator OnnxMul to the graph.
`__ne__`	Automatically adds operator OnnxNot x OnnxEqual to the graph.
`__ne__`	Automatically adds operator OnnxNot x OnnxEqual to the graph.
`__ne__`	Automatically adds operator OnnxNot x OnnxEqual to the graph.
`__pow__`	Automatically adds operator OnnxPow to the graph.
`__pow__`	Automatically adds operator OnnxPow to the graph.
`__pow__`	Automatically adds operator OnnxPow to the graph.
`__repr__`	usual
`__repr__`	usual
`__repr__`	usual
`__repr__`	usual
`__repr__`	usual
`__repr__`
`__str__`	usual
`__sub__`	Automatically adds operator OnnxSub to the graph.
`__sub__`	Automatically adds operator OnnxSub to the graph.
`__sub__`	Automatically adds operator OnnxSub to the graph.
`__truediv__`	Automatically adds operator OnnxDiv to the graph.
`__truediv__`	Automatically adds operator OnnxDiv to the graph.
`__truediv__`	Automatically adds operator OnnxDiv to the graph.
`_add_domain`
`_add_name`
`_add_subgraph`	Fills attribute attribute.
`_add_subgraph`	Fills attribute attribute.
`_add_subgraph`	Fills attribute attribute.
`_check`
`_check`
`_check`
`_node_to_graph`	Builds a graph as a list of nodes to walk through in that order.
`_node_to_graph`	Builds a graph as a list of nodes to walk through in that order.
`_node_to_graph`	Builds a graph as a list of nodes to walk through in that order.
`_post_process_attributes`	Walks through attributes and replaces them by ONNX values.
`_post_process_attributes`	Walks through attributes and replaces them by ONNX values.
`_post_process_attributes`	Walks through attributes and replaces them by ONNX values.
`_process_io`
`_set_control_op`
`_set_control_op`	Sets control_op for every instance of `OnnxExisting` node.
`_set_control_op`	Tells this operator is part of a subgraph.
`_set_control_op`	Sets control_op for every instance of `OnnxExisting` node.
`_set_control_op`	Tells this operator is part of a subgraph.
`_set_control_op`	Tells this operator is part of a subgraph.
`_to_onnx_attribute`	Converts one subgraph into ONNX. Returns the ONNX graph and the hidden inputs.
`_to_onnx_attribute`	Converts one subgraph into ONNX. Returns the ONNX graph and the hidden inputs.
`_to_onnx_attribute`	Converts one subgraph into ONNX. Returns the ONNX graph and the hidden inputs.
`_to_onnx_attributes`	Converts attributes into ONNX. Returns the hidden inputs.
`_to_onnx_attributes`	Converts attributes into ONNX. Returns the hidden inputs.
`_to_onnx_attributes`	Converts attributes into ONNX. Returns the hidden inputs.
`_to_onnx_attributes`	Calls self.onx_op._to_onnx_attributes.
`_to_onnx_attributes`	Calls self.onx_op._to_onnx_attributes.
`add_external_input`	Tells a subgraph this node comes from a graph calling this one.
`add_external_input`	Tells a subgraph this node comes from a graph calling this one.
`add_external_input`	Tells a subgraph this node comes from the main graph. It may be used only by the subgraph but it must be processed …
`add_external_input`	Tells a subgraph this node comes from a graph calling this one.
`add_external_input`	Tells a subgraph this node comes from the main graph. It may be used only by the subgraph but it must be processed …
`add_external_input`	Tells a subgraph this node comes from the main graph. It may be used only by the subgraph but it must be processed …
`add_function`	Adds a function to the graph.
`add_initializer`	Adds an initializer to the graph.
`add_node`	Adds a node to the graph.
`add_to`	Adds to graph builder.
`add_to`	Adds to graph builder.
`add_to`	This method should be overwritten.
`add_to`	Adds to graph builder.
`add_to`	Adds to graph builder. Does nothing because the original node is already added.
`add_to`	Adds to graph builder. Does nothing because the original node is already added.
`and_`	Automatically adds operator OnnxAnd to the graph.
`and_`	Automatically adds operator OnnxAnd to the graph.
`and_`	Automatically adds operator OnnxAnd to the graph.
`astype`	Automatically adds operator OnnxCast to the graph.
`astype`	Automatically adds operator OnnxCast to the graph.
`astype`	Automatically adds operator OnnxCast to the graph.
`data`	Returns all data in that class in a dictionary.
`debug`	debug
`debug`	debug
`debug`	debug
`dedent`	unindent
`dedent`	unindent
`do`	Fills attribute body.
`do`	Fills attribute body.
`do`	Fills attribute body.
`else_do`	Fills attribute else_branch.
`else_do`	Fills attribute else_branch.
`else_do`	Fills attribute else_branch.
`f`	For the eager mode.
`f`	Computes the predictions for this node. Similar to an eager evaluation.
`f`	Evaluates this node.
`f`	Computes the predictions for this node. Similar to an eager evaluation.
`f`	Computes the predictions for this node. Similar to an eager evaluation.
`f`	Evaluates this node.
`find_named_inputs`	Retrieves all named inputs in this graph.
`find_named_inputs`	Retrieves all named inputs in this graph.
`find_named_inputs`	Returns all inputs to the graph.
`find_named_inputs`	Retrieves all named inputs in this graph.
`find_named_inputs`	Returns all inputs to the graph.
`find_named_inputs`	Returns all inputs to the graph.
`find_schema`	Checks if there is an existing schema for a specific version.
`find_schema`	Checks if there is an existing schema for a specific version.
`find_schema`	Checks if there is an existing schema for a specific version.
`get_input_names`	Returns input names for node node and inputs inputs.
`get_output_result`	Returns the output name at position i.
`get_output_result`	Returns the output name at position i.
`get_output_result`	Returns the output name at position i.
`get_output_result`	Returns the output name at position i.
`get_unique_name`	Returns a unique name to name an output.
`get_unique_output_name`	Returns a unique output_name for a NodeResultName.
`indent`	indent
`indent`	indent
`not_`	Automatically adds operator OnnxNot to the graph.
`not_`	Automatically adds operator OnnxNot to the graph.
`not_`	Automatically adds operator OnnxNot to the graph.
`or_`	Automatically adds operator OnnxOr to the graph.
`or_`	Automatically adds operator OnnxOr to the graph.
`or_`	Automatically adds operator OnnxOr to the graph.
`populate`	Populates static variables.
`predecessors`	Returns the list of predecessors.
`predecessors`	Returns the list of predecessors.
`predecessors`	Returns the list of predecessors.
`reserve_name`	Reserves a name so that it cannot be changed.
`reserve_names`	Adds names to the list of reserved names. All must be unique.
`run`	Other name for OnnxInference.f.
`run`	Other name for OnnxInference.f.
`run`	Other name for OnnxInference.f.
`SerializeToString`	Serializes this class into json.
`then_do`	Fills attribute then_branch.
`then_do`	Fills attribute then_branch.
`then_do`	Fills attribute then_branch.
`to_onnx`	Converts this operator into an ONNX graph.
`to_onnx`	Converts this operator into an ONNX graph.
`to_onnx`	Converts this operator into an ONNX graph.
`to_onnx`	Converts this operator into an ONNX graph. It follows the same signature as `OnnxOperator.to_onnx` …
`to_onnx`	Converts this operator into an ONNX graph.
`to_onnx_this`	Returns a simple ONNX graph corresponding to this node.
`to_onnx_this`	Returns a simple ONNX graph corresponding to this node.
`to_onnx_this`	Returns a simple ONNX graph corresponding to this node.
`update_max_item`	Some operators return a undefined number of outputs. The method is called when require one of them (with __getitem__) …
`update_max_item`	Some operators return a undefined number of outputs. The method is called when require one of them (with __getitem__) …
`update_max_item`	Some operators return a undefined number of outputs. The method is called when require one of them (with __getitem__) …

Documentation#

Xop API to build onnx graphs. Inspired from sklearn-onnx.

New in version 0.9.

source on GitHub

mlprodict.npy.xop.ClassFactory(class_name, op_name, inputs, outputs, input_range, output_range, domain, attr_names, doc, deprecated, since_version, past_version)#

Dynamically creates a class for a specific operator.

Parameters:

class_name – class name
op_name – operator type
inputs – expected inputs
outputs – expected outputs
input_range – input range
output_range – output_range
domain – domain
attr_names – attributes names
doc – docstring
deprecated – is the operator deprecated
since_version – available since version
past_version – list of versions

source on GitHub

class mlprodict.npy.xop.OnnxExisting(*args, **kwargs)#

Bases: OnnxOperator

Wrapper around OnnxIdentity to specify this operator is not part of the subgraph it is used in.

source on GitHub

__init__(*args, **kwargs)#

__repr__()#

usual

source on GitHub

_set_control_op(op, subgraph_inputs=None)#

Sets control_op for every instance of OnnxExisting node.

Parameters:

op – operator calling the subgraph.
inputs – additional parameters to convert into ONNX

source on GitHub

_unique_names = {}#

f(*inputs, verbose=0, fLOG=None, clear_cache=False, runtime=None)#: For the eager mode.

find_named_inputs()#

Retrieves all named inputs in this graph.

source on GitHub

static get_unique_name(var)#

Returns a unique variable name.

Parameters:: var – an instance of OnnxOperator.
Returns:: unique variable name

source on GitHub

class mlprodict.npy.xop.OnnxLoadFactory#

Bases: object

Automatically creating all operators from onnx packages takes time. That’s why function loadop only creates classes for the requested operators. This class does the same when an attributes is requested.

cl = OnnxLoadOperators()
x = cl.Add(...)

It is equivalent to:

OnnxAdd = loadop('Add')
x = OnnxAdd(...)

source on GitHub

__getattr__(name)#

Enables expressions such as:

ops = OnnxLoadFactory()
op = ops.Abs('X')

source on GitHub

__init__()#

class mlprodict.npy.xop.OnnxOperator(*inputs, op_version=None, output_names=None, domain=None, global_context=None, **kwargs)#

Bases: OnnxOperatorBase

Ancestor to every ONNX operator exposed in mlprodict.npy.xops and mlprodict.npy.xops_ml.

Parameters:

inputs – list of inputs expected by the operator
op_version – to select a specific version of the operator
output_names – used defined names for the outputs
domain – to overwrite the default domain
global_context – operator If executes one subgraph whose nodes may use one existing output in the current context. If not used in the main graph, these operators are not linked to the output and cannot be retrieved. global_context is a dictionary mapped the subgraph input names to these operators.
kwargs – additional parameters of the operator

source on GitHub

class _InputContainer#

Bases: object

__init__()#

__iter__()#

__len__()#

__repr__()#: Return repr(self).

append(inp)#: Append one element to the list.

has_input(inp)#: Checks that input inp is part the list of names.

__abs__()#

Automatically adds operator OnnxAbs to the graph.

Parameters:: ov – onnx node
Returns:: OnnxAbs(self, ov)

source on GitHub

__add__(ov)#

Automatically adds operator OnnxAdd to the graph.

Parameters:: ov – onnx node
Returns:: OnnxAdd(self, ov)

source on GitHub

__call__(*args, function_name=None, function_domain=None, **kwargs)#

Creates an instance of class OnnxOperatorFunction. Equivalent to OnnxOperatorFunction(proto, *args, **kwargs).

Parameters:

args – see OnnxOperatorFunction
function_name – name to be given to the function
function_domain – function domain, if None, it is given a default value
kwargs – see OnnxOperatorFunction

Returns:

instance of type OnnxOperatorFunction

source on GitHub

classmethod __class_getitem__(opset)#

Enables expression cls[opset]. It returns the appropriate class cls_opset. Parameter op_version should be specified.

source on GitHub

__eq__(ov)#

Automatically adds operator OnnxEqual to the graph.

Parameters:: ov – onnx node
Returns:: OnnxEqual(self, ov)

source on GitHub

__ge__(ov)#

Automatically adds operator OnnxGreaterOrEqual to the graph.

Parameters:: ov – onnx node
Returns:: OnnxGreater(self, ov)

source on GitHub

__getitem__(index)#

Returns an accessor to one of the output of this node.

source on GitHub

__gt__(ov)#

Automatically adds operator OnnxGreater to the graph.

Parameters:: ov – onnx node
Returns:: OnnxGreater(self, ov)

source on GitHub

__hash__ = None#

__init__(*inputs, op_version=None, output_names=None, domain=None, global_context=None, **kwargs)#

__iter__()#

Allows expressions such as a, b = OnnxTopK(...).

source on GitHub

__le__(ov)#

Automatically adds operator OnnxLess to the graph.

Parameters:: ov – onnx node
Returns:: OnnxLess(self, ov)

source on GitHub

__lt__(ov)#

Automatically adds operator OnnxLess to the graph.

Parameters:: ov – onnx node
Returns:: OnnxLess(self, ov)

source on GitHub

__matmul__(ov)#

Automatically adds operator OnnxMatMul to the graph.

Parameters:: ov – onnx node
Returns:: OnnMatMul(self, ov)

source on GitHub

__mod__(ov)#

Automatically adds operator OnnxMod to the graph.

Parameters:: ov – onnx node
Returns:: OnnxMod(self, ov)

source on GitHub

__mul__(ov)#

Automatically adds operator OnnxMul to the graph.

Parameters:: ov – onnx node
Returns:: OnnxMul(self, ov)

source on GitHub

__ne__(ov)#

Automatically adds operator OnnxNot x OnnxEqual to the graph.

Parameters:: ov – onnx node
Returns:: OnnxNot(OnnxEqual(self, ov))

source on GitHub

__pow__(ov)#

Automatically adds operator OnnxPow to the graph.

Parameters:: ov – onnx node
Returns:: OnnPow(self, ov)

source on GitHub

__repr__()#

usual

source on GitHub

__sub__(ov)#

Automatically adds operator OnnxSub to the graph.

Parameters:: ov – onnx node
Returns:: OnnxSub(self, ov)

source on GitHub

__truediv__(ov)#

Automatically adds operator OnnxDiv to the graph.

Parameters:: ov – onnx node
Returns:: OnnxDiv(self, ov)

source on GitHub

_add_subgraph(attribute, branch, subgraph_inputs=None)#

Fills attribute attribute.

Parameters:

attribute – attribute name
branch – onnx graph or OnnxOperator
subgraph_inputs – additional parameter to convert the subgraph into ONNX

Returns:

self

source on GitHub

_check()#

static _merge_op_version(n1, n2, at_least=None)#

_node_to_graph(other_outputs=None, inputs=None, outputs=None, as_function=False, processed=None)#

Builds a graph as a list of nodes to walk through in that order.

source on GitHub

static _node_to_graph_get_type(node, name=None, outputs=None, outputs_dtype=None)#

static _node_to_graph_preprocess_list(inputs)#

static _node_to_graph_process_input(processed, inputs, set_inputs, node, inp, new_inputs, new_stack, inputs_dtype, as_function=False)#

static _node_to_graph_reorder_by_name(new_inputs, inputs)#

_post_process_attributes()#

Walks through attributes and replaces them by ONNX values.

source on GitHub

_set_control_op(op, subgraph_inputs=None)#

Sets control_op for every instance of OnnxExisting node.

Parameters:

op – operator calling the subgraph.
inputs – additional parameters to convert into ONNX

source on GitHub

_to_onnx_attribute(att_name, oxop, inputs=None, target_opset=None, optim=True, verbose=0, run_shape=True, fLOG=<built-in function print>, processed=None)#

Converts one subgraph into ONNX. Returns the ONNX graph and the hidden inputs.

source on GitHub

_to_onnx_attributes(inputs=None, target_opset=None, optim=True, verbose=0, run_shape=True, fLOG=<built-in function print>, processed=None)#

Converts attributes into ONNX. Returns the hidden inputs.

source on GitHub

add_external_input(op)#

Tells a subgraph this node comes from a graph calling this one.

source on GitHub

add_to(builder)#

Adds to graph builder.

Parameters:: builder – instance of _GraphBuilder, it must have a method add_node

source on GitHub

and_(ov)#

Automatically adds operator OnnxAnd to the graph.

Parameters:: ov – onnx node
Returns:: OnnxAnd(self, ov)

source on GitHub

astype(to)#

Automatically adds operator OnnxCast to the graph.

Parameters:: ov – onnx node
Returns:: OnnxCast(self, ov, to=to)

source on GitHub

do(body, subgraph_inputs=None)#

Fills attribute body.

Parameters:

branch – onnx graph or OnnxOperator
subgraph_inputs – additional parameter to convert the subgraph into ONNX

Returns:

self

source on GitHub

else_do(branch)#

Fills attribute else_branch.

Parameters:: branch – onnx graph or OnnxOperator
Returns:: self

source on GitHub

f(*inputs, verbose=0, fLOG=None, clear_cache=False, runtime=None)#

Computes the predictions for this node. Similar to an eager evaluation.

Parameters:

inputs – inputs as dictionary or a list of inputs (see below)
verbose – display information while predicting
fLOG – logging function if verbose > 0
clear_cache – onnx graph is created once unless this parameter is True
runtime – runtime to use for the evaluation, see OnnxInference

Returns:

outputs as a dictionary if the input were given as a dictionary or a single result or a tuple otherwise

The inputs refer to the inputs of the graph. The method walks through all inputs and finds inputs defined as string. It replaces them by the value found in the dictionary. If the inputs are specified in a list, the function retrieves the list of inputs defined as a string and assigns them a value. Logging function can be used to get more insight about it. During the evaluation every node is independently converted into ONNX. The ONNX graph is cached in the class itself.

source on GitHub

find_named_inputs()#

Retrieves all named inputs in this graph.

source on GitHub

find_schema(op_version)#

Checks if there is an existing schema for a specific version.

Parameters:: op_version – requested version
Returns:: schema

source on GitHub

get_output_result(i=0)#

Returns the output name at position i.

source on GitHub

not_()#

Automatically adds operator OnnxNot to the graph.

Parameters:: ov – onnx node
Returns:: OnnxNot(self, ov)

source on GitHub

or_(ov)#

Automatically adds operator OnnxOr to the graph.

Parameters:: ov – onnx node
Returns:: OnnxOr(self, ov)

source on GitHub

property output_names#: Returns self.output_names_.

predecessors()#

Returns the list of predecessors.

Returns:: list of OnnxOperator

source on GitHub

run(*inputs, verbose=0, fLOG=None, clear_cache=False, runtime=None)#

Other name for OnnxInference.f.

source on GitHub

then_do(branch)#

Fills attribute then_branch.

Parameters:: branch – onnx graph or OnnxOperator
Returns:: self

source on GitHub

to_onnx(inputs=None, outputs=None, other_outputs=None, target_opset=None, optim=True, verbose=0, run_shape=True, function_name=None, function_domain=None, fLOG=<built-in function print>, processed=None, check_model=True, return_builder=False)#

Converts this operator into an ONNX graph.

Parameters:

inputs – information about type, it should not be None
outputs – information about types, if None, the function will use shape inference to guess the final output type and shape
other_outputs – additional nodes to consider as graph outputs but not outputs of this particular node
target_opset – dictionary with target opset per domain, None for the default one
optim – optimize the model with function onnx_optimisations
run_shape – in case output shapes are not specify, the function runs function infer_shapes to guess them, False would disable that default behaviour
verbose – prints information
function_name – if not None, returns a FunctionProto
function_domain – in case of a function, declares the function as part of this domain
fLOG – logging function
processed – keeps track the of the processed nodes
check_model – checks the output model
return_builder – if True, returns the instance of GraphBuilder used to build the onnx graph.

Returns:

ONNX stucture

inputs and outputs parameters work the same way. Here is some possible walues:

inputs=numpy.float32: all inputs are dense tensors of unknown shapes sharing the same element type
inputs={‘X’: numpy.float32, ‘Y’: numpy.in64}`: input X is a dense tensor of float32, input Y is a dense tensor of int64,
{‘X’: numpy.array(…)}}: input X is a dense tensor with a precise shape
inputs=[Variable(‘X’, numpy.float32, [1, 2])]: input X is a dense tensor of float32 with shape [1, 2]
inputs=[Variable(‘X’, numpy.float32, [None, 2])]: input X is a dense tensor of float32 with a 2D tensor with an unknown dimension (first one)
see Variable

(OnnxOperator)

source on GitHub

to_onnx_this(evaluated_inputs)#

Returns a simple ONNX graph corresponding to this node.

Parameters:: evaluated_inputs – inputs as a list
Returns:: ONNX graph

(OnnxOperator)

source on GitHub

update_max_item(index)#

Some operators return a undefined number of outputs. The method is called when require one of them (with __getitem__) and keeps the greater requested index assuming the node does not output any result beyond that index.

Parameters:: index – requested index

source on GitHub

class mlprodict.npy.xop.OnnxOperatorBase#

Bases: object

Base class for OnnxOperator, OnnxOperator.

source on GitHub

__init__()#

_set_control_op(op, subgraph_inputs=None)#

Tells this operator is part of a subgraph.

source on GitHub

add_external_input(op)#

Tells a subgraph this node comes from the main graph. It may be used only by the subgraph but it must be processed as well.

source on GitHub

add_to(builder)#: This method should be overwritten.

f(*args, **kwargs)#

Evaluates this node.

source on GitHub

find_named_inputs()#

Returns all inputs to the graph.

source on GitHub

property output_names#: This method should be overwritten.

class mlprodict.npy.xop.OnnxOperatorFunction(function_proto, *inputs, output_names=None, sub_functions=None)#

Bases: OnnxOperator

This operator is used to insert existing ONNX function into the ONNX graph being built.

Parameters:

function_proto – instance of type FunctionProto
inputs – inputs
output_names – output names
sub_functions – functions called by this one

source on GitHub

__init__(function_proto, *inputs, output_names=None, sub_functions=None)#

__repr__()#: usual

add_to(builder)#

Adds to graph builder.

Parameters:: builder – instance of _GraphBuilder, it must have a method add_node

source on GitHub

static attribute_to_value(att)#

Converts an attribute into a value using python structures.

source on GitHub

class mlprodict.npy.xop.OnnxOperatorItem(onx_op, index, op_version=None)#

Bases: OnnxOperatorBase

Accessor to one of the output returned by a OnnxOperator.

Parameters:

onx_op – OnnxOperator
index – integer
op_version – defines the opset version

source on GitHub

__init__(onx_op, index, op_version=None)#

__repr__()#: usual

__str__()#: usual

_to_onnx_attributes(inputs=None, target_opset=None, optim=True, verbose=0, run_shape=True, fLOG=<built-in function print>, processed=None)#

Calls self.onx_op._to_onnx_attributes.

source on GitHub

add_to(builder)#

Adds to graph builder. Does nothing because the original node is already added.

Parameters:: builder – instance of _GraphBuilder, it must have a method add_node

source on GitHub

f(*inputs, verbose=0, fLOG=None, clear_cache=False, runtime=None)#

Computes the predictions for this node. Similar to an eager evaluation.

Parameters:

inputs – inputs as dictionary or a list of inputs (see below)
verbose – display information while predicting
fLOG – logging function if verbose > 0
clear_cache – onnx graph is created once unless this parameter is True
runtime – runtime to use for the evaluation, see OnnxInference

Returns:

outputs as a dictionary if the input were given as a dictionary or a single result or a tuple otherwise

The inputs refer to the inputs of the graph. The method walks through all inputs and finds inputs defined as string. It replaces them by the value found in the dictionary. If the inputs are specified in a list, the function retrieves the list of inputs defined as a string and assigns them a value. Logging function can be used to get more insight about it. During the evaluation every node is independently converted into ONNX. The ONNX graph is cached in the class itself.

source on GitHub

find_named_inputs()#

Returns all inputs to the graph.

source on GitHub

get_output_result(i=0)#

Returns the output name at position i.

source on GitHub

property inputs#: Returns the only inputs in a list.

property output_names#: Returns None.

class mlprodict.npy.xop.OnnxOperatorTuple(first, *args)#

Bases: OnnxOperatorBase

Class used to return multiple OnnxVar at the same time.

source on GitHub

__getitem__(i)#: usual

__init__(first, *args)#

__iter__()#: Iterates on the outputs.

__len__()#: usual

__repr__()#: usual

_set_control_op(op, subgraph_inputs=None)#

Tells this operator is part of a subgraph.

source on GitHub

_to_onnx_attributes(inputs=None, target_opset=None, optim=True, verbose=0, run_shape=True, fLOG=<built-in function print>, processed=None)#

Calls self.onx_op._to_onnx_attributes.

source on GitHub

add_to(builder)#

Adds to graph builder. Does nothing because the original node is already added.

Parameters:: builder – instance of _GraphBuilder, it must have a method add_node

source on GitHub

property external_inputs#

Returns the list of implicit inputs the subgraph assumes to be existing even if they are not referenced as explicit input for the graph.

source on GitHub

find_named_inputs()#

Returns all inputs to the graph.

source on GitHub

property inputs#: Returns the only inputs in a list.

property output_names#: Returns ‘output_names’ of attribute ‘unique’.

property outputs#: Returns ‘output_names’ of attribute ‘unique’.

to_onnx(inputs=None, outputs=None, other_outputs=None, target_opset=None, optim=True, verbose=0, run_shape=True, processed=None, check_model=True, return_builder=False, fLOG=None)#

Converts this operator into an ONNX graph. It follows the same signature as OnnxOperator.to_onnx and calls this method of the unique input object or the first one if there are several. In that case, other inputs in attribute values are moved into container other_outputs.

(OnnxOperatorTuple)

source on GitHub

class mlprodict.npy.xop._CustomSchema(schema)#

Bases: object

For operators defined outside onnx.

source on GitHub

static ParseFromString(s)#: Parses this class from a json string.

SerializeToString()#: Serializes this class into json.

__eq__(ot)#: Return self==value.

__hash__ = None#

__init__(schema)#

__repr__()#: Return repr(self).

class _attribute(att)#

Bases: object

attribute

__eq__(ot)#: Return self==value.

__hash__ = None#

__init__(att)#

data()#: Returns all data in that class in a dictionary.

_atts = ['domain', 'name', 'since_version', 'inputs', 'outputs', 'attributes', 'min_input', 'max_input', 'min_output', 'max_output', 'doc']#

class _empty#

Bases: object

dummy class

static from_attribute(data)#: Creates an instance of _CustomSchema._attribute.

static from_io(data)#: Creates an instance of _CustomSchema._io.

class _io(t)#

Bases: object

input, output

__eq__(ot)#: Return self==value.

__hash__ = None#

__init__(t)#

data()#: Returns all data in that class in a dictionary.

data()#: Returns all data in that class in a dictionary.

class mlprodict.npy.xop._GraphBuilder#

Bases: object

Graph builder. It takes a graph structure made with instances of OnnxOperatorBase. The main method is to_onnx.

initializer: list of initializers to add to the ONNX graph
node: list of nodes to add to the ONNX graph
input: list of inputs to add to the ONNX graph
output: list of inputs to add to the ONNX graph
opsets: opsets of the ONNX graph
input_names: dictionary of input names
{name: InputDetectedVariable}
node_output_names: memorizes a name for a node output
when the user did not specify any {(id(node), index): OutputDetectedVariable}
reserved_names: dictionary { name : (node, index) },
name which should remain unchanged in the ONNX graph
names: list of uniques names
functions: dictionary { domain, name: function_proto }
function_hashes: dictionary { domain, name: hash of function_proto }

source on GitHub

__init__()#

_add_domain(domain, version)#

_add_name(name)#

_process_io(inputs, input_names_)#

add_function(function_proto, raise_if_exist=False, check_unique=True, opset=1)#

Adds a function to the graph.

Parameters:

function_proto – instance of type FunctionProto
raise_if_exist – raises an exception if a function of the same name was already added
check_unique – checks if a function was added twice, it is the same
opset – opset for the domain the function belongs to

source on GitHub

add_initializer(name, init)#

Adds an initializer to the graph.

Parameters:

name – initializer name
init – initializer to copy

Returns:

created intializer

source on GitHub

add_node(op_type, name, inputs, outputs, domain='', opset=None, **attributes)#

Adds a node to the graph.

Parameters:

op_type – operator type
name – node name
inputs – inputs name list
outputs – outputs name list
domain – node domain
opset – node opset

Returns:

created node

source on GitHub

get_input_names(node, inputs)#

Returns input names for node node and inputs inputs.

Parameters:

node – node
inputs – inputs

Returns:

name

source on GitHub

get_unique_name(name, reserved=True)#

Returns a unique name to name an output.

Parameters:

name – name
reserved – bypass if the name is a reserved one

Returns:

unique name, may be the same if not taken already

source on GitHub

get_unique_output_name(result)#

Returns a unique output_name for a NodeResultName.

Parameters:: result – instance of NodeResultName

source on GitHub

static number2alpha(index)#

Converts a numbers into a string keeping the same alphabetical order.

source on GitHub

reserve_name(node, name, index)#

Reserves a name so that it cannot be changed.

Parameters:

node – node or None for an input
name – name
index – input index

source on GitHub

reserve_names(node, output_names)#

Adds names to the list of reserved names. All must be unique.

Parameters:

node – node or None for an input
output_names – names of the output

source on GitHub

to_onnx(inputs=None, outputs=None, target_opset=None, run_shape=False, optim=True, function_name=None, function_domain=None, verbose=0, check_model=True)#

Converts this operator into an ONNX graph.

Parameters:

inputs – specific inputs (as a dictionary) or default inputs if not specified
outputs – specific outputs
target_opset – dictionary with target opset per domain, None for the default one
run_shape – run shape inference before returning the model
optim – optimize the model with function onnx_optimisations
function_name – if not None builds a FunctionProto use this name
function_domain – in case of a function, declares the function as part of this domain, ‘mlprodict’ if None
verbose – prints information
check_model – checks the output model

Returns:

onnx graph

(_GraphBuilder)

source on GitHub

class mlprodict.npy.xop._StaticVariables#

Bases: object

Holds static variables.

source on GitHub

__init__()#

property all_classes#: Returns all operators wrapped in classes.

property all_domains#: Returns all domains.

property all_schemas#: Returns all schemas.

property all_schemas_versions#: Returns all operators, domains, versions.

populate()#: Populates static variables.

class mlprodict.npy.xop._WrapperLogger(lg)#

Bases: object

Wrappers around class logging.Logger to take indentation into account.

source on GitHub

constructor

__init__(lg)#: constructor

debug(msg, *args)#

dedent()#: unindent

indent()#

class mlprodict.npy.xop._WrapperPrint#

Bases: _WrapperLogger

Wrappers around print to help debugging.

source on GitHub

constructor

__init__()#: constructor

debug(msg, *args, indent=None)#

mlprodict.npy.xop._default_OPSET_TO_IR_VERSION()#

Returns the default mapping between opset and ir_version.

<<<

import pprint
from mlprodict.npy.xop import _default_OPSET_TO_IR_VERSION
pprint.pprint(_default_OPSET_TO_IR_VERSION())

>>>

    {1: 3,
3,
3,
3,
3,
3,
3,
4,
4,
5,
6,
7,
7,
7,
8,
8,
8}

source on GitHub

mlprodict.npy.xop._domain_to_class_name(domain)#

Converts domain into a name.

Parameters:: domain – domain name such as ai.onnx.ml
Returns:: string

<<<

from mlprodict.npy.xop import _domain_to_class_name
print(_domain_to_class_name('ai.onnx.ml'))

>>>

    AiOnnxMl

source on GitHub

mlprodict.npy.xop._dynamic_class_creation(operator_names=None, cache=False, include_past=False, verbose=0, fLOG=<built-in function print>)#

Automatically generates classes for each of the operators module onnx defines and described at Operators and Operators.

Parameters:

operator_names – list of operators to request or None for all
cache – extract the documentation from onnx package and saves it on disk it True
include_past – includes past versions if operator_names is None
verbose – display some progress
fLOG – logging function

Returns:

list of requested operators as a tuple

source on GitHub

mlprodict.npy.xop._find_operator_domain(name)#

Determines the domain of an operator. Raises an exception if not found or if there is an ambiguity.

Parameters:: name – operator name
Returns:: domain

source on GitHub

mlprodict.npy.xop._get_all_operator_schema()#

mlprodict.npy.xop._populate_schemas()#

Populates all schemas.

source on GitHub

mlprodict.npy.xop._split_op_name(name)#

mlprodict.npy.xop.loadop(*names, cache=False, verbose=0, fLOG=<built-in function print>)#

Dynamically creates a class for a every operator type in the given list.

source on GitHub

mlprodict.npy.xop.local_print(msg, *args, indent=None)#: debug

module npy.xop#

Short summary#

Classes#

Functions#

Properties#

Static Methods#

Methods#

Documentation#

module `npy.xop`#