a0cbf1c44a
* Reimplement conditions * Refactor datasets and implement LabelBatch --------- Co-authored-by: Dario Coscia <dariocos99@gmail.com>
122 lines
4.2 KiB
Python
122 lines
4.2 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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Instanciate the correct subclass of InputTargetCondition by checking the
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type of 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 or 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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:return: InputTargetCondition subclass
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:rtype: TensorInputTensorTargetCondition or
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TensorInputGraphTargetCondition or GraphInputTensorTargetCondition
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or GraphInputGraphTargetCondition
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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 or 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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"""
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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 torch.Tensor 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 torch.Tensor input and Graph/Data target
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data.
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"""
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class GraphInputTensorTargetCondition(InputTargetCondition):
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"""
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InputTargetCondition subclass for Graph/Data input and torch.Tensor target
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data.
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"""
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class GraphInputGraphTargetCondition(InputTargetCondition):
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"""
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InputTargetCondition subclass for Graph/Data input and target data.
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"""
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