NodeProto#

class onnx_light.onnx.NodeProto(*args, **kwargs)#

Computation graphs are made up of a DAG of nodes, which represent what is commonly called a ‘layer’ or ‘pipeline stage’ in machine learning frameworks. For example, it can be a node of type ‘Conv’ that takes in an image, a filter tensor and a bias tensor, and produces the convolved output.

ByteSize(self) → int#: Returns the serialized size in bytes, following the protobuf API.

CopyFrom(self, arg: onnx_light.onnx_py._onnxpy.NodeProto, /) → None#: Copies one instance into this one.

ParseFromFile(self, name: str, options: object | None = None, external_data_file: str = '') → None#: Parses a binary file to fill this instance.

ParseFromString(self, data: bytes, options: object | None = None) → None#

ParseFromString(self, data: str, options: object | None = None) → None

Overloaded function.

ParseFromString(self, data: bytes, options: object | None = None) -> None

Parses a sequence of bytes to fill this instance.

ParseFromString(self, data: str, options: object | None = None) -> None

Parses a string to fill this instance.

SerializeSize(self, options: object | None = None) → onnx_light.onnx_py._onnxpy.SerializeSizeResult#: Returns the size once serialized without serializing.

SerializeToFile(self, name: str, options: object | None = None, external_data_file: str = '') → None#: Serializes this instance into a file. If external_data_size is not empty, big weights are stored in this (depending on options.raw_data_threshold). When writing to two files, temporary external-data metadata is cleared so the in-memory model stays unchanged.

SerializeToString(self, options: object | None = None) → bytes#: Serializes this instance into a sequence of bytes.

property attribute#: Attributes associated with this node.

property device_configurations#: Configuration of multi-device annotations.

property doc_string#: A human-readable documentation for this node. Markdown is allowed.

property domain#: The domain of the OperatorSet that specifies the operator named by op_type.

has_attribute(self) → bool#: Tells if ‘attribute’ has a value.

has_device_configurations(self) → bool#: Tells if ‘device_configurations’ has a value.

has_doc_string(self) → bool#: Tells if ‘doc_string’ has a value

has_domain(self) → bool#: Tells if ‘domain’ has a value

has_input(self) → bool#: Tells if ‘input’ has a value.

has_metadata_props(self) → bool#: Tells if ‘metadata_props’ has a value.

has_name(self) → bool#: Tells if ‘name’ has a value

has_op_type(self) → bool#: Tells if ‘op_type’ has a value

has_output(self) → bool#: Tells if ‘output’ has a value.

has_overload(self) → bool#: Tells if ‘overload’ has a value

property input#: inputs of the node

property metadata_props#: Named metadata values; keys should be distinct.

property name#: An optional identifier for this node in a graph. This field MAY be absent in this version of the IR.

property op_type#: The symbolic identifier of the Operator to execute.

property output#: outputs of the node

property overload#: Overload identifier, used only to map this to a model-local function.