module `onnx_tools._onnx_check_model`#

Short summary#

module mlprodict.onnx_tools._onnx_check_model

Python implementation of onnx.checker.check_model.

Classes#

class	truncated documentation
`CheckerContext`	Class hosting information about a graph.
`CheckerContextDefaultRegistry`	Registry.
`LexicalScopeContext`	Construct an instance with the lexical scope from the parent graph to allow lookup of names from that scope via this_or_ancestor_graph_has. …
`OnnxCheckError`	Raised when a model fails check.
`Schema`	Wrapper around a schema.
`UndefinedSchema`	Undefined schema.

Functions#

function	truncated documentation
`_check_data_field`
`_check_field`
`_check_function`
`_check_graph`
`_check_map`
`_check_model`
`_check_model_local_functions`
`_check_node`
`_check_opset_compatibility`
`_check_optional`
`_check_sequence`
`_check_sparse_tensor`
`_check_sparse_tensor_indices_1`	Check that the index data stored in a SparseTensorProto is valid. indices: a 1-dimensional tensor; indices[i] represents …
`_check_sparse_tensor_indices_2`	Check that the index data stored in a SparseTensorProto is valid. indices: a 2-dimensional tensor; indices[i,j] represents …
`_check_tensor`
`_check_value_info`
`_enforce_has_field`
`_enforce_has_repeated_field`
`_enforce_non_empty_field`
`_get_version_for_domain`
`_parse_data`
`check_attribute`	NB: This is a generic “attribute well-formedness” check, it doesn’t actually test if an attribute is valid per a schema. …
`check_is_experimental_op`	Tells if an operator is experimentation.
`check_model`	Checks a model is consistent with ONNX language. The function fails if the model is not consistent.

Properties#

property	truncated documentation
`deprecated_`	Returns False.

Methods#

method	truncated documentation
`__getattr__`
`__init__`
`__init__`
`__init__`
`__init__`
`__init__`
`add`	Adds a name to the context.
`copy`	Copies the instance.
`get_ir_version`	Accessor.
`get_model_dir`	Accessor.
`get_opset_imports`	Accessor.
`get_schema`	Accessor.
`get_schema_registry`	Accessor.
`GetSchema`	Accessor.
`is_main_graph`	Accessor.
`num_inputs_allowed`	Not implemented yet.
`num_outputs_allowed`	Not implemented yet.
`set_ir_version`	Accessor.
`set_is_main_graph`	Accessor.
`set_model_dir`	Accessor.
`set_opset_imports`	Accessor.
`set_schema_registry`	Accessor.
`this_graph_has`	Checks the context includes a specific name.
`this_or_ancestor_graph_has`	Checks the context and its ancestor includes a specific name.
`verify`	Verifies a node is consistent with ONNX language.
`verify`	Verifies a, undefined node is consistent with ONNX language.

Documentation#

Python implementation of onnx.checker.check_model.

source on GitHub

class mlprodict.onnx_tools._onnx_check_model.CheckerContext(ctx=None)#

Bases: object

Class hosting information about a graph.

source on GitHub

__init__(ctx=None)#

get_ir_version()#: Accessor.

get_model_dir()#: Accessor.

get_opset_imports()#: Accessor.

get_schema_registry()#: Accessor.

is_main_graph()#: Accessor.

set_ir_version(v)#: Accessor.

set_is_main_graph(is_main_graph)#: Accessor.

set_model_dir(model_dir)#: Accessor.

set_opset_imports(imps)#: Accessor.

set_schema_registry(schema_registry)#: Accessor.

class mlprodict.onnx_tools._onnx_check_model.CheckerContextDefaultRegistry#

Bases: object

Registry.

source on GitHub

GetSchema(op_type, version, domain)#: Accessor.

get_schema(op_type, version, domain)#: Accessor.

class mlprodict.onnx_tools._onnx_check_model.LexicalScopeContext(parent_context=None)#

Bases: object

Construct an instance with the lexical scope from the parent graph to allow lookup of names from that scope via this_or_ancestor_graph_has. The caller must ensure parent_context remains valid for the entire lifetime of the new instance. Alternatively, if that cannot be guaranteed, create an instance with the default constructor and populate output_names with the values from the parent scope so the values are copied instead.

source on GitHub

__init__(parent_context=None)#

add(name)#: Adds a name to the context.

copy()#: Copies the instance.

this_graph_has(name)#: Checks the context includes a specific name.

this_or_ancestor_graph_has(name)#: Checks the context and its ancestor includes a specific name.

exception mlprodict.onnx_tools._onnx_check_model.OnnxCheckError(msg, proto)#

Bases: RuntimeError

Raised when a model fails check.

Parameters:

msg – message
proto – proto

source on GitHub

__init__(msg, proto)#

class mlprodict.onnx_tools._onnx_check_model.Schema(schema)#

Bases: object

Wrapper around a schema.

source on GitHub

__getattr__(attr)#

__init__(schema)#

num_inputs_allowed(n)#: Not implemented yet.

num_outputs_allowed(n)#: Not implemented yet.

verify(node)#: Verifies a node is consistent with ONNX language.

class mlprodict.onnx_tools._onnx_check_model.UndefinedSchema(name, version, domain)#

Bases: object

Undefined schema.

source on GitHub

__init__(name, version, domain)#

property deprecated_#: Returns False.

verify(node)#: Verifies a, undefined node is consistent with ONNX language.

mlprodict.onnx_tools._onnx_check_model._check_data_field(tensor, field, num_value_fields)#

mlprodict.onnx_tools._onnx_check_model._check_field(tensor, field, value_field, nelem)#

mlprodict.onnx_tools._onnx_check_model._check_function(function, ctx, parent_lex)#

mlprodict.onnx_tools._onnx_check_model._check_graph(graph, ctx, parent_lex)#

mlprodict.onnx_tools._onnx_check_model._check_map(map, ctx)#

mlprodict.onnx_tools._onnx_check_model._check_model(model, ctx)#

mlprodict.onnx_tools._onnx_check_model._check_model_local_functions(model, ctx, parent_lex)#

mlprodict.onnx_tools._onnx_check_model._check_node(node, ctx, lex_ctx)#

mlprodict.onnx_tools._onnx_check_model._check_opset_compatibility(node, ctx, func_opset_imports, model_opset_imports)#

mlprodict.onnx_tools._onnx_check_model._check_optional(optional, ctx)#

mlprodict.onnx_tools._onnx_check_model._check_sequence(sequence, ctx)#

mlprodict.onnx_tools._onnx_check_model._check_sparse_tensor(sparse_tensor_proto, ctx)#

mlprodict.onnx_tools._onnx_check_model._check_sparse_tensor_indices_1(indices, sparse_tensor_proto, nnz)#

Check that the index data stored in a SparseTensorProto is valid. indices: a 1-dimensional tensor; indices[i] represents the linearized index value for the i-th nonzero value.

source on GitHub

mlprodict.onnx_tools._onnx_check_model._check_sparse_tensor_indices_2(indices, sparse_tensor_proto, nnz)#

Check that the index data stored in a SparseTensorProto is valid. indices: a 2-dimensional tensor; indices[i,j] represents the j-th index value for the i-th nonzero value.

source on GitHub

mlprodict.onnx_tools._onnx_check_model._check_tensor(tensor, ctx)#

mlprodict.onnx_tools._onnx_check_model._check_value_info(value_info, ctx)#

mlprodict.onnx_tools._onnx_check_model._enforce_has_field(proto, field)#

mlprodict.onnx_tools._onnx_check_model._enforce_has_repeated_field(proto, field)#

mlprodict.onnx_tools._onnx_check_model._enforce_non_empty_field(proto, field)#

mlprodict.onnx_tools._onnx_check_model._get_version_for_domain(domain, opset_imports)#

mlprodict.onnx_tools._onnx_check_model._parse_data(dtype, indices)#

mlprodict.onnx_tools._onnx_check_model.check_attribute(attr, ctx, lex_ctx)#

NB: This is a generic “attribute well-formedness” check, it doesn’t actually test if an attribute is valid per a schema.

source on GitHub

mlprodict.onnx_tools._onnx_check_model.check_is_experimental_op(node_op_type)#: Tells if an operator is experimentation.

mlprodict.onnx_tools._onnx_check_model.check_model(model)#

Checks a model is consistent with ONNX language. The function fails if the model is not consistent.

Parameters:: model – ModelProto

source on GitHub

module onnx_tools._onnx_check_model#

Short summary#

Classes#

Functions#

Properties#

Methods#

Documentation#

module `onnx_tools._onnx_check_model`#