136 lines
4.8 KiB
Python
136 lines
4.8 KiB
Python
"""
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This module contains condition classes for supervised learning tasks.
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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 ..label_tensor import LabelTensor
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from ..graph import Graph
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from .condition_interface import ConditionInterface
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class InputTargetCondition(ConditionInterface):
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"""
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Condition for domain/equation data. This condition must be used every
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time a Physics Informed or a Supervised Loss is needed in the Solver.
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"""
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__slots__ = ["input", "target"]
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_avail_input_cls = (torch.Tensor, LabelTensor, Data, Graph, list, tuple)
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_avail_output_cls = (torch.Tensor, LabelTensor, Data, Graph, list, tuple)
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def __new__(cls, input, target):
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"""
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Instantiate the appropriate subclass of InputTargetCondition based on
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the types of input and target data.
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:param input: Input data for the condition.
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:type input: torch.Tensor | Graph | Data | list | tuple
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:param target: Target data for the condition.
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Graph, Data, or list/tuple.
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:type target: torch.Tensor | Graph | Data | list | tuple
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:return: Subclass of InputTargetCondition
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:rtype: TensorInputTensorTargetCondition |
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TensorInputGraphTargetCondition |
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GraphInputTensorTargetCondition |
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GraphInputGraphTargetCondition
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:raises ValueError: If input and or target are not of type
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:class:`torch.Tensor`, :class:`LabelTensor`, :class:`Graph`, or
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:class:`Data`.
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"""
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if cls != InputTargetCondition:
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return super().__new__(cls)
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if isinstance(input, (torch.Tensor, LabelTensor)) and isinstance(
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target, (torch.Tensor, LabelTensor)
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):
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subclass = TensorInputTensorTargetCondition
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return subclass.__new__(subclass, input, target)
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if isinstance(input, (torch.Tensor, LabelTensor)) and isinstance(
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target, (Graph, Data, list, tuple)
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):
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cls._check_graph_list_consistency(target)
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subclass = TensorInputGraphTargetCondition
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return subclass.__new__(subclass, input, target)
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if isinstance(input, (Graph, Data, list, tuple)) and isinstance(
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target, (torch.Tensor, LabelTensor)
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):
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cls._check_graph_list_consistency(input)
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subclass = GraphInputTensorTargetCondition
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return subclass.__new__(subclass, input, target)
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if isinstance(input, (Graph, Data, list, tuple)) and isinstance(
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target, (Graph, Data, list, tuple)
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):
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cls._check_graph_list_consistency(input)
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cls._check_graph_list_consistency(target)
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subclass = GraphInputGraphTargetCondition
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return subclass.__new__(subclass, input, target)
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raise ValueError(
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"Invalid input/target types. "
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"Please provide either Data, Graph, LabelTensor or torch.Tensor "
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"objects."
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)
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def __init__(self, input, target):
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"""
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Initialize the InputTargetCondition, storing the input and target data.
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:param input: torch.Tensor or Graph/Data object containing the input
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:type input: torch.Tensor | Graph or Data
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:param target: torch.Tensor or Graph/Data object containing the target
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:type target: torch.Tensor or Graph or Data
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.. note::
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If either ``input`` or ``target`` are composed by a list of
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:class:`Graph`/:class:`Data` objects, all elements must have the
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same structure (keys and data types)
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"""
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super().__init__()
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self._check_input_target_len(input, target)
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self.input = input
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self.target = target
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@staticmethod
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def _check_input_target_len(input, target):
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if isinstance(input, (Graph, Data)) or isinstance(
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target, (Graph, Data)
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):
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return
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if len(input) != len(target):
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raise ValueError(
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"The input and target lists must have the same length."
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)
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class TensorInputTensorTargetCondition(InputTargetCondition):
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"""
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InputTargetCondition subclass for :class:`torch.Tensor`/:class:`LabelTensor`
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input and target data.
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"""
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class TensorInputGraphTargetCondition(InputTargetCondition):
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"""
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InputTargetCondition subclass for :class:`torch.Tensor`/:class:`LabelTensor`
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input and :class:`Graph`/:class:`Data` target data.
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"""
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class GraphInputTensorTargetCondition(InputTargetCondition):
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"""
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InputTargetCondition subclass for :class:`Graph`/:class:`Data` input and
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:class:`torch.Tensor`/:class:`LabelTensor` target data.
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"""
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class GraphInputGraphTargetCondition(InputTargetCondition):
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"""
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InputTargetCondition subclass for :class:`Graph`/:class:`Data` input and
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target data.
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"""
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