def __init__(self,
op: str,
name: str = None,
attrs: Dict[str, object] = None,
inputs: List["Tensor"] = None,
outputs: List["Tensor"] = None):
"""
A node represents an operation in a graph, and consumes zero or more Tensors, and produces zero or more Tensors.
Args:
op (str): The operation this node performs.
name (str): The name of this node.
attrs (Dict[str, object]): A dictionary that maps attribute names to their values.
inputs (List[Tensor]): A list of zero or more input Tensors.
outputs (List[Tensor]): A list of zero or more output Tensors.
"""
self.op = op
self.name = misc.default_value(name, "")
self.attrs = misc.default_value(attrs, OrderedDict())
self.inputs = misc.SynchronizedList(self,
field_name="outputs",
initial=misc.default_value(
inputs, []))
self.outputs = misc.SynchronizedList(self,
field_name="inputs",
initial=misc.default_value(
outputs, []))
def __init__(self,
name: str,
values: Union[np.ndarray, LazyValues],
data_location: int = None):
"""
Represents a Tensor whose value is known.
Args:
name (str): The name of the tensor.
values (numpy.ndarray): The values in this tensor, in the form of a NumPy array.
data_location (int):
An enum value indicating the location where the tensor data is stored.
Generally, this will come from onnx.TensorProto.DataLocation.
"""
self.name = name
self.inputs = misc.SynchronizedList(self,
field_name="outputs",
initial=[])
self.outputs = misc.SynchronizedList(self,
field_name="inputs",
initial=[])
if not isinstance(values, np.ndarray) and not isinstance(
values, LazyValues):
G_LOGGER.critical(
"Provided `values` argument is not a NumPy array or a LazyValues instance. "
"Please provide a NumPy array or LazyValues instance to construct a Constant. "
"Note: Provided `values` parameter was: {:}".format(values))
self._values = values
self.data_location = data_location
def __init__(self, name: str, dtype: np.dtype=None, shape: Sequence[Union[int, str]]=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.
"""
self.name = name
self.inputs = misc.SynchronizedList(self, field_name="outputs", initial=[])
self.outputs = misc.SynchronizedList(self, field_name="inputs", initial=[])
self.dtype = dtype
self.shape = misc.default_value(shape, None)
def __init__(self, name: str, values: np.ndarray):
"""
Represents a Tensor whose value is known.
Args:
name (str): The name of the tensor.
values (numpy.ndarray): The values in this tensor, in the form of a NumPy array.
dtype (numpy.dtype): The data type of the tensor.
shape (Sequence[Union[int, str]]): The shape of the tensor.
"""
self.name = name
self.inputs = misc.SynchronizedList(self, field_name="outputs", initial=[])
self.outputs = misc.SynchronizedList(self, field_name="inputs", initial=[])
if not isinstance(values, np.ndarray):
G_LOGGER.critical("Provided `values` argument is not a NumPy array (please provide a NumPy array to construct a Constant): {:}".format(values))
self.values = np.array(values)