SequenceProto#

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

Defines a dense, ordered, collection of elements that are of homogeneous types. Sequences can be made out of tensors, maps, or sequences. If a sequence is made out of tensors, the tensors must have the same element type (i.e. int32). In some cases, the tensors in a sequence can have different shapes. Whether the tensors can have different shapes or not depends on the type/shape associated with the corresponding ValueInfo. For example, Sequence<Tensor<float, [M,N]> means that all tensors have same shape. However, Sequence<Tensor<float, [omitted,omitted]> means they can have different shapes (all of rank 2), where omitted means the corresponding dimension has no symbolic/constant value. Finally, Sequence<Tensor<float, omitted>> means that the different tensors can have different ranks, when the shape itself is omitted from the tensor-type. For a more complete description

ByteSize(self) → int#

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

CopyFrom(self, arg: onnx_light.onnx_py._onnxpy.SequenceProto, /) → None#

Copies one instance into this one.

class DataType(*values)#
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.

  1. 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 elem_type#

The type of the elements in the sequence. The type of each element MUST match the elem_type specified. This field MUST be present for this version of the IR.

has_map_values(self) → bool#

Tells if ‘map_values’ has a value.

has_name(self) → bool#

Tells if ‘name’ has a value

has_optional_values(self) → bool#

Tells if ‘optional_values’ has a value.

has_sequence_values(self) → bool#

Tells if ‘sequence_values’ has a value.

has_sparse_tensor_values(self) → bool#

Tells if ‘sparse_tensor_values’ has a value.

has_tensor_values(self) → bool#

Tells if ‘tensor_values’ has a value.

property map_values#

For MapProto values. When this field is present, the elem_type field MUST be MAP.

property name#

An optional identifier for this sequence.

property optional_values#

For Optional values. When this field is present, the elem_type field MUST be OPTIONAL.

property sequence_values#

For SequenceProto values. When this field is present, the elem_type field MUST be SEQUENCE.

property sparse_tensor_values#

For SparseTensorProto values. When this field is present, the elem_type field MUST be SPARSE_TENSOR.

property tensor_values#

For TensorProto values. When this field is present, the elem_type field MUST be TENSOR.