new conditions
This commit is contained in:
Dario Coscia
committed by
Nicola Demo
Nicola Demo
parent
a888141707
commit
fd16fcf9b4
@@ -1,27 +1,21 @@
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""" Condition module. """
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from ..label_tensor import LabelTensor
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from ..domain import DomainInterface
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from ..equation.equation import Equation
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from . import DomainOutputCondition, DomainEquationCondition
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def dummy(a):
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"""Dummy function for testing purposes."""
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return None
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from .domain_equation_condition import DomainEquationCondition
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from .input_equation_condition import InputPointsEquationCondition
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from .input_output_condition import InputOutputPointsCondition
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from .data_condition import DataConditionInterface
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class Condition:
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"""
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The class ``Condition`` is used to represent the constraints (physical
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equations, boundary conditions, etc.) that should be satisfied in the
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problem at hand. Condition objects are used to formulate the PINA :obj:`pina.problem.abstract_problem.AbstractProblem` object.
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Conditions can be specified in three ways:
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problem at hand. Condition objects are used to formulate the
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PINA :obj:`pina.problem.abstract_problem.AbstractProblem` object.
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Conditions can be specified in four ways:
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1. By specifying the input and output points of the condition; in such a
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case, the model is trained to produce the output points given the input
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points.
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points. Those points can either be torch.Tensor, LabelTensors, Graph
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2. By specifying the location and the equation of the condition; in such
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a case, the model is trained to minimize the equation residual by
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@@ -29,79 +23,48 @@ class Condition:
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3. By specifying the input points and the equation of the condition; in
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such a case, the model is trained to minimize the equation residual by
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evaluating it at the passed input points.
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evaluating it at the passed input points. The input points must be
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a LabelTensor.
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4. By specifying only the data matrix; in such a case the model is
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trained with an unsupervised costum loss and uses the data in training.
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Additionaly conditioning variables can be passed, whenever the model
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has extra conditioning variable it depends on.
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Example::
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>>> example_domain = Span({'x': [0, 1], 'y': [0, 1]})
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>>> def example_dirichlet(input_, output_):
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>>> value = 0.0
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>>> return output_.extract(['u']) - value
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>>> example_input_pts = LabelTensor(
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>>> torch.tensor([[0, 0, 0]]), ['x', 'y', 'z'])
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>>> example_output_pts = LabelTensor(torch.tensor([[1, 2]]), ['a', 'b'])
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>>>
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>>> Condition(
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>>> input_points=example_input_pts,
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>>> output_points=example_output_pts)
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>>> Condition(
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>>> location=example_domain,
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>>> equation=example_dirichlet)
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>>> Condition(
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>>> input_points=example_input_pts,
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>>> equation=example_dirichlet)
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>>> TODO
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"""
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# def _dictvalue_isinstance(self, dict_, key_, class_):
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# """Check if the value of a dictionary corresponding to `key` is an instance of `class_`."""
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# if key_ not in dict_.keys():
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# return True
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__slots__ = list(
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set(
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InputOutputPointsCondition.__slots__,
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InputPointsEquationCondition.__slots__,
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DomainEquationCondition.__slots__,
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DataConditionInterface.__slots__
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# return isinstance(dict_[key_], class_)
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# def __init__(self, *args, **kwargs):
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# """
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# Constructor for the `Condition` class.
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# """
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# self.data_weight = kwargs.pop("data_weight", 1.0)
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# if len(args) != 0:
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# raise ValueError(
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# f"Condition takes only the following keyword arguments: {Condition.__slots__}."
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# )
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)
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)
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def __new__(cls, *args, **kwargs):
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if sorted(kwargs.keys()) == sorted(["input_points", "output_points"]):
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return DomainOutputCondition(
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domain=kwargs["input_points"],
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output_points=kwargs["output_points"]
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if len(args) != 0:
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raise ValueError(
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f"Condition takes only the following keyword '
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'arguments: {Condition.__slots__}."
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)
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elif sorted(kwargs.keys()) == sorted(["domain", "output_points"]):
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return DomainOutputCondition(**kwargs)
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elif sorted(kwargs.keys()) == sorted(["domain", "equation"]):
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sorted_keys = sorted(kwargs.keys())
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if sorted_keys == sorted(InputOutputPointsCondition.__slots__):
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return InputOutputPointsCondition(**kwargs)
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elif sorted_keys == sorted(InputPointsEquationCondition.__slots__):
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return InputPointsEquationCondition(**kwargs)
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elif sorted_keys == sorted(DomainEquationCondition.__slots__):
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return DomainEquationCondition(**kwargs)
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elif sorted_keys == sorted(DataConditionInterface.__slots__):
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return DataConditionInterface(**kwargs)
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elif sorted_keys == DataConditionInterface.__slots__[0]:
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return DataConditionInterface(**kwargs)
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else:
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raise ValueError(f"Invalid keyword arguments {kwargs.keys()}.")
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# TODO: remove, not used anymore
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'''
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if (
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sorted(kwargs.keys()) != sorted(["input_points", "output_points"])
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and sorted(kwargs.keys()) != sorted(["location", "equation"])
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and sorted(kwargs.keys()) != sorted(["input_points", "equation"])
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):
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raise ValueError(f"Invalid keyword arguments {kwargs.keys()}.")
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# TODO: remove, not used anymore
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if not self._dictvalue_isinstance(kwargs, "input_points", LabelTensor):
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raise TypeError("`input_points` must be a torch.Tensor.")
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if not self._dictvalue_isinstance(kwargs, "output_points", LabelTensor):
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raise TypeError("`output_points` must be a torch.Tensor.")
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if not self._dictvalue_isinstance(kwargs, "location", Location):
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raise TypeError("`location` must be a Location.")
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if not self._dictvalue_isinstance(kwargs, "equation", Equation):
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raise TypeError("`equation` must be a Equation.")
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for key, value in kwargs.items():
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setattr(self, key, value)
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'''
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raise ValueError(f"Invalid keyword arguments {kwargs.keys()}.")
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