91 lines
3.2 KiB
Python
91 lines
3.2 KiB
Python
"""
|
|
DataCondition class
|
|
"""
|
|
|
|
import torch
|
|
from torch_geometric.data import Data
|
|
from .condition_interface import ConditionInterface
|
|
from ..label_tensor import LabelTensor
|
|
from ..graph import Graph
|
|
|
|
|
|
class DataCondition(ConditionInterface):
|
|
"""
|
|
This condition must be used every time a Unsupervised Loss is needed in
|
|
the Solver. The conditionalvariable can be passed as extra-input when
|
|
the model learns a conditional distribution.
|
|
"""
|
|
|
|
__slots__ = ["input", "conditional_variables"]
|
|
_avail_input_cls = (torch.Tensor, LabelTensor, Data, Graph, list, tuple)
|
|
_avail_conditional_variables_cls = (torch.Tensor, LabelTensor)
|
|
|
|
def __new__(cls, input, conditional_variables=None):
|
|
"""
|
|
Instantiate the appropriate subclass of DataCondition based on the
|
|
types of input data.
|
|
|
|
:param input: Input data for the condition.
|
|
:type input: torch.Tensor | LabelTensor | Graph |
|
|
Data | list[Graph] | list[Data] | tuple[Graph] | tuple[Data]
|
|
:param conditional_variables: Conditional variables for the condition.
|
|
:type conditional_variables: torch.Tensor | LabelTensor
|
|
:return: Subclass of DataCondition.
|
|
:rtype: TensorDataCondition | GraphDataCondition
|
|
|
|
:raises ValueError: If input is not of type :class:`torch.Tensor`,
|
|
:class:`pina.label_tensor.LabelTensor`, :class:`pina.graph.Graph`,
|
|
or :class:`~torch_geometric.data.Data`.
|
|
|
|
|
|
"""
|
|
if cls != DataCondition:
|
|
return super().__new__(cls)
|
|
if isinstance(input, (torch.Tensor, LabelTensor)):
|
|
subclass = TensorDataCondition
|
|
return subclass.__new__(subclass, input, conditional_variables)
|
|
|
|
if isinstance(input, (Graph, Data, list, tuple)):
|
|
cls._check_graph_list_consistency(input)
|
|
subclass = GraphDataCondition
|
|
return subclass.__new__(subclass, input, conditional_variables)
|
|
|
|
raise ValueError(
|
|
"Invalid input types. "
|
|
"Please provide either torch_geometric.data.Data or Graph objects."
|
|
)
|
|
|
|
def __init__(self, input, conditional_variables=None):
|
|
"""
|
|
Initialize the object by storing the input and conditional
|
|
variables (if any).
|
|
|
|
:param input: Input data for the condition.
|
|
:type input: torch.Tensor | LabelTensor | Graph | Data | list[Graph] |
|
|
list[Data] | tuple[Graph] | tuple[Data]
|
|
:param conditional_variables: Conditional variables for the condition.
|
|
:type conditional_variables: torch.Tensor or LabelTensor
|
|
|
|
.. note::
|
|
If either `input` is composed by a list of :class:`pina.graph.Graph`
|
|
or :class:`~torch_geometric.data.Data` objects, all elements must
|
|
have the same structure (keys and data types)
|
|
"""
|
|
super().__init__()
|
|
self.input = input
|
|
self.conditional_variables = conditional_variables
|
|
|
|
|
|
class TensorDataCondition(DataCondition):
|
|
"""
|
|
DataCondition for :class:`torch.Tensor` or
|
|
:class:`pina.label_tensor.LabelTensor` input data
|
|
"""
|
|
|
|
|
|
class GraphDataCondition(DataCondition):
|
|
"""
|
|
DataCondition for :class:`pina.graph.Graph` or
|
|
:class:`~torch_geometric.data.Data` input data
|
|
"""
|