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