GraphProto

class onnx_light.onnx.GraphProto(*args, **kwargs)

A graph defines the computational logic of a model and is comprised of a parameterized list of nodes that form a directed acyclic graph based on their inputs and outputs. This is the equivalent of the ‘network’ or ‘graph’ in many deep learning frameworks.

ByteSize(self) → int

Returns the serialized size in bytes, following the protobuf API.

CopyFrom(self, arg: onnx_light.onnx_py._onnxpy.GraphProto, /) → 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.

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

Parses a sequence of bytes to fill this instance.

2. 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 doc_string

A human-readable documentation for this graph. Markdown is allowed.

has_doc_string(self) → bool

Tells if ‘doc_string’ has a value

has_initializer(self) → bool

Tells if ‘initializer’ 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_node(self) → bool

Tells if ‘node’ has a value.

has_output(self) → bool

Tells if ‘output’ has a value.

has_quantization_annotation(self) → bool

Tells if ‘quantization_annotation’ has a value.

has_sparse_initializer(self) → bool

Tells if ‘sparse_initializer’ has a value.

has_value_info(self) → bool

Tells if ‘value_info’ has a value.

property initializer

A list of named sparse tensor values, used to specify constant inputs of the graph. Each initializer (both TensorProto as well SparseTensorProto) MUST have a name. The name MUST be unique across both initializer and sparse_initializer, but the name MAY also appear in the input list.

property input

Inputs of the graph, shapes and types are optional in a subgraph and mandatory in the main graph.

property metadata_props

Named metadata values; keys should be distinct.

property name

The name of the graph.

property node

The nodes in the graph, sorted topologically.

property output

Outputs of the graph, shapes and types are optional in a subgraph and mandatory in the main graph.

property quantization_annotation

For tensor ‘a’, it may have {‘SCALE_TENSOR’, ‘a_scale’} and {‘ZERO_POINT_TENSOR’, ‘a_zero_point’} annotated, which means, tensor ‘a_scale’ and tensor ‘a_zero_point’ are scale and zero point of tensor ‘a’ in the model.

Type:

This field carries information to indicate the mapping among a tensor and its quantization parameter tensors. For example

property sparse_initializer

A list of named tensor values, used to specify constant inputs of the graph. Each initializer (both TensorProto as well SparseTensorProto) MUST have a name. The name MUST be unique across both initializer and sparse_initializer, but the name MAY also appear in the input list.

property value_info

Information for the values in the graph. The ValueInfoProto.name’s must be distinct. It is optional for a value to appear in value_info list.