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)