SparseTensorProto#

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

A sparse tensor.

ByteSize(self) → int#

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

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

  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 dims#

The shape.

has_dims(self) → bool#

Tells if ‘dims’ has a value.

has_indices(self) → bool#

Tells if ‘indices’ has a value.

has_values(self) → bool#

Tells if ‘values’ has a value.

property indices#

e.g., index-value [1,4] must appear before [2,1].

Type:

The indices of the non-default values, which may be stored in one of two formats. (a) Indices can be a tensor of shape [NNZ, rank] with the [i,j]-th value corresponding to the j-th index of the i-th value (in the values tensor). (b) Indices can be a tensor of shape [NNZ], in which case the i-th value must be the linearized-index of the i-th value (in the values tensor). The linearized-index can be converted into an index tuple (k_1,…,k_rank) using the shape provided below. The indices must appear in ascending order without duplication. In the first format, the ordering is lexicographic-ordering

property values#

The sequence of non-default values are encoded as a tensor of shape [NNZ]. The default-value is zero for numeric tensors, and empty-TypeProto::TensorString for string tensors. values must have a non-empty name present which serves as a name for SparseTensorProto when used in sparse_initializer list.