onnx_graphsurgeon.ir.tensor.Variable Class Reference

Inheritance diagram for onnx_graphsurgeon.ir.tensor.Variable:

Collaboration diagram for onnx_graphsurgeon.ir.tensor.Variable:

Public Member Functions
def	__init__ (self, str name, np.dtype dtype=None, Sequence[Union[int, str]] shape=None)
def	to_constant (self, np.ndarray values)
def	copy (self)
def	__setattr__ (self, name, value)
def	is_empty (self)
def	to_variable (self, np.dtype dtype=None, Sequence[Union[int, str]] shape=[])
def	i (self, tensor_idx=0, producer_idx=0)
def	o (self, consumer_idx=0, tensor_idx=0)
def	__str__ (self)
def	__repr__ (self)
def	__eq__ (self, other)

Static Public Member Functions
def	empty ()

Public Attributes
	name
	inputs
	outputs
	dtype
	shape
	values

Static Public Attributes
int	DYNAMIC = -1

Private Attributes
	__class__

Constructor & Destructor Documentation

__init__()

def onnx_graphsurgeon.ir.tensor.Variable.__init__	(		self,
		str	name,
		np.dtype	dtype = None,
		Sequence[Union[int, str]]	shape = None
	)

Represents a Tensor whose value is not known until inference-time.

Args:
    name (str): The name of the tensor.
    dtype (numpy.dtype): The data type of the tensor.
    shape (Sequence[Union[int, str]]): The shape of the tensor. This may contain strings if the model uses dimension parameters.

Member Function Documentation

empty()

def onnx_graphsurgeon.ir.tensor.Variable.empty ( )

static

to_constant()

def onnx_graphsurgeon.ir.tensor.Variable.to_constant	(		self,
		np.ndarray	values
	)

Modifies this tensor in-place to convert it to a Constant. This means that all consumers/producers of the tensor will see the update.

Args:
    values (np.ndarray): The values in this tensor

Returns:
    self

Reimplemented from onnx_graphsurgeon.ir.tensor.Tensor.

copy()

def onnx_graphsurgeon.ir.tensor.Variable.copy

(

self

)

Makes a shallow copy of this tensor, omitting input and output information.

Note: Generally, you should only ever make a deep copy of a Graph.

__setattr__()

def onnx_graphsurgeon.ir.tensor.Tensor.__setattr__ (   self,   name,   value  )

inherited

is_empty()

def onnx_graphsurgeon.ir.tensor.Tensor.is_empty (   self )

inherited

Returns whether this tensor is considered empty in the graph.

*Note: 'Empty' here refers to the name of the tensor, which is omitted for
optional tensors, NOT the shape of the tensor*

Returns:
    bool: Whether the tensor is empty, meaning that it is used for an omitted optional input or output.

to_variable()

def onnx_graphsurgeon.ir.tensor.Tensor.to_variable (   self, np.dtype  dtype = None, Sequence[Union[int, str]]  shape = []  )

inherited

Modifies this tensor in-place to convert it to a Variable. This means that all consumers/producers of the tensor will see the update.

Args:
    dtype (np.dtype): The data type of the tensor.
    shape (Sequence[int]): The shape of the tensor.

Returns:
    self

Reimplemented in onnx_graphsurgeon.ir.tensor.Constant.

i()

def onnx_graphsurgeon.ir.tensor.Tensor.i (   self,   tensor_idx = 0,   producer_idx = 0  )

inherited

Convenience function to get an input tensor of one of this tensor's input nodes.
Note that the parameters are swapped compared to the o() function; this is because tensors are likely to have only a single producer

For example:
::

    assert tensor.i() == tensor.inputs[0].inputs[0]
    assert tensor.i(1, 2) == tensor.inputs[2].inputs[1]

Args:
    tensor_idx (int): The index of the input tensor of the input node. Defaults to 0.
    producer_idx (int): The index of the producer node of the input tensor, if the tensor has multiple producers. Defaults to 0.

Returns:
    Tensor: The specified producer (input) tensor.

o()

def onnx_graphsurgeon.ir.tensor.Tensor.o (   self,   consumer_idx = 0,   tensor_idx = 0  )

inherited

Convenience function to get an output tensor of one of this tensor's output nodes.

For example:
::

    assert tensor.o() == tensor.outputs[0].outputs[0]
    assert tensor.o(2, 1) == tensor.outputs[2].outputs[1]

Args:
    consumer_idx (int): The index of the consumer of the input tensor. Defaults to 0.
    tensor_idx (int): The index of the output tensor of the node, if the node has multiple outputs. Defaults to 0.

Returns:
    Tensor: The specified consumer (output) tensor

__str__()

def onnx_graphsurgeon.ir.tensor.Tensor.__str__ (   self )

inherited

Here is the caller graph for this function:

__repr__()

def onnx_graphsurgeon.ir.tensor.Tensor.__repr__ (   self )

inherited

Reimplemented in onnx_graphsurgeon.ir.tensor.Constant.

Here is the call graph for this function:

__eq__()

def onnx_graphsurgeon.ir.tensor.Tensor.__eq__ (   self,   other  )

inherited

Perform a check to see if two tensors are equal.

Tensors are considered equal if they share the same name. A Graph must not include Tensors with duplicate names.

Member Data Documentation

name

onnx_graphsurgeon.ir.tensor.Variable.name

inputs

onnx_graphsurgeon.ir.tensor.Variable.inputs

outputs

onnx_graphsurgeon.ir.tensor.Variable.outputs

dtype

onnx_graphsurgeon.ir.tensor.Variable.dtype

shape

onnx_graphsurgeon.ir.tensor.Variable.shape

DYNAMIC

int onnx_graphsurgeon.ir.tensor.Tensor.DYNAMIC = -1

staticinherited

__class__

onnx_graphsurgeon.ir.tensor.Tensor.__class__

privateinherited

values

onnx_graphsurgeon.ir.tensor.Tensor.values

inherited

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The documentation for this class was generated from the following file:

• tensor.py