a0cbf1c44a
* Reimplement conditions * Refactor datasets and implement LabelBatch --------- Co-authored-by: Dario Coscia <dariocos99@gmail.com>
110 lines
3.9 KiB
Python
110 lines
3.9 KiB
Python
"""
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Module to define InputEquationCondition class and its subclasses.
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"""
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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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from ..utils import check_consistency
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from ..equation.equation_interface import EquationInterface
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class InputEquationCondition(ConditionInterface):
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"""
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Condition for input/equation data. This condition must be used every
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time a Physics Informed Loss is needed in the Solver.
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"""
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__slots__ = ["input", "equation"]
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_avail_input_cls = (LabelTensor, Graph, list, tuple)
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_avail_equation_cls = EquationInterface
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def __new__(cls, input, equation):
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"""
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Instanciate the correct subclass of InputEquationCondition by checking
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the type of the input data (only `input`).
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:param input: torch.Tensor or Graph/Data object containing the input
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:type input: torch.Tensor or Graph or Data
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:param EquationInterface equation: Equation object containing the
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equation function
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:return: InputEquationCondition subclass
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:rtype: InputTensorEquationCondition or InputGraphEquationCondition
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"""
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# If the class is already a subclass, return the instance
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if cls != InputEquationCondition:
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return super().__new__(cls)
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# Instanciate the correct subclass
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if isinstance(input, (Graph, Data, list, tuple)):
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subclass = InputGraphEquationCondition
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cls._check_graph_list_consistency(input)
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subclass._check_label_tensor(input)
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return subclass.__new__(subclass, input, equation)
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if isinstance(input, LabelTensor):
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subclass = InputTensorEquationCondition
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return subclass.__new__(subclass, input, equation)
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# If the input is not a LabelTensor or a Graph object raise an error
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raise ValueError(
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"The input data object must be a LabelTensor or a Graph object."
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)
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def __init__(self, input, equation):
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"""
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Initialize the InputEquationCondition by storing the input and equation.
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:param input: torch.Tensor or Graph/Data object containing the input
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:type input: torch.Tensor or Graph or Data
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:param EquationInterface equation: Equation object containing the
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equation function
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"""
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super().__init__()
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self.input = input
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self.equation = equation
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def __setattr__(self, key, value):
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if key == "input":
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check_consistency(value, self._avail_input_cls)
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InputEquationCondition.__dict__[key].__set__(self, value)
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elif key == "equation":
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check_consistency(value, self._avail_equation_cls)
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InputEquationCondition.__dict__[key].__set__(self, value)
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elif key in ("_problem"):
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super().__setattr__(key, value)
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class InputTensorEquationCondition(InputEquationCondition):
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"""
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InputEquationCondition subclass for LabelTensor input data.
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"""
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class InputGraphEquationCondition(InputEquationCondition):
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"""
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InputEquationCondition subclass for Graph input data.
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"""
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@staticmethod
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def _check_label_tensor(input):
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"""
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Check if the input is a LabelTensor.
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:param input: input data
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:type input: torch.Tensor or Graph or Data
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"""
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# Store the fist element of the list/tuple if input is a list/tuple
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# it is anougth to check the first element because all elements must
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# have the same type and structure (already checked)
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data = input[0] if isinstance(input, (list, tuple)) else input
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# Check if the input data contains at least one LabelTensor
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for v in data.values():
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if isinstance(v, LabelTensor):
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return
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raise ValueError(
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"The input data object must contain at least one LabelTensor."
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)
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