Lightining update (#104)
* multiple functions for version 0.0 * lightining update * minor changes * data pinn loss added --------- Co-authored-by: Nicola Demo <demo.nicola@gmail.com> Co-authored-by: Dario Coscia <dariocoscia@cli-10-110-3-125.WIFIeduroamSTUD.units.it> Co-authored-by: Dario Coscia <dariocoscia@Dario-Coscia.station> Co-authored-by: Dario Coscia <dariocoscia@Dario-Coscia.local> Co-authored-by: Dario Coscia <dariocoscia@192.168.1.38>
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Dario Coscia
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Nicola Demo
Nicola Demo
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pina/loss.py
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127
pina/loss.py
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""" Module for EquationInterface class """
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from abc import ABCMeta, abstractmethod
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from torch.nn.modules.loss import _Loss
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import torch
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from .utils import check_consistency
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__all__ = ['LpLoss']
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class LossInterface(_Loss, metaclass=ABCMeta):
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"""
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The abstract `LossInterface` class. All the class defining a PINA Loss
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should be inheritied from this class.
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"""
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def __init__(self, reduction = 'mean'):
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"""
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:param str reduction: Specifies the reduction to apply to the output:
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``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction
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will be applied, ``'mean'``: the sum of the output will be divided
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by the number of elements in the output, ``'sum'``: the output will
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be summed. Note: :attr:`size_average` and :attr:`reduce` are in the
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process of being deprecated, and in the meantime, specifying either of
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those two args will override :attr:`reduction`. Default: ``'mean'``.
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"""
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super().__init__(reduction=reduction, size_average=None, reduce=None)
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@abstractmethod
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def forward(self):
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pass
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def _reduction(self, loss):
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"""Simple helper function to check reduction
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:param reduction: Specifies the reduction to apply to the output:
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``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction
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will be applied, ``'mean'``: the sum of the output will be divided
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by the number of elements in the output, ``'sum'``: the output will
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be summed. Note: :attr:`size_average` and :attr:`reduce` are in the
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process of being deprecated, and in the meantime, specifying either of
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those two args will override :attr:`reduction`. Default: ``'mean'``.
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:type reduction: str, optional
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:param loss: Loss tensor for each element.
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:type loss: torch.Tensor
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:return: Reduced loss.
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:rtype: torch.Tensor
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"""
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if self.reduction == "none":
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ret = loss
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elif self.reduction == "mean":
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ret = torch.mean(loss, keepdim=True, dim=-1)
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elif self.reduction == "sum":
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ret = torch.sum(loss, keepdim=True, dim=-1)
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else:
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raise ValueError(self.reduction + " is not valid")
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return ret
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class LpLoss(LossInterface):
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"""
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The Lp loss implementation class. Creates a criterion that measures
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the Lp error between each element in the input :math:`x` and
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target :math:`y`.
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The unreduced (i.e. with :attr:`reduction` set to ``'none'``) loss can
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be described as:
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.. math::
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\ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad
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l_n = \left| x_n - y_n \right|^p,
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If ``'relative'`` is set to true:
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.. math::
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\ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad
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l_n = \left[\frac{\left| x_n - y_n \right|^p}{\left|y_n \right|^p}\right]^{1/p},
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where :math:`N` is the batch size. If :attr:`reduction` is not ``'none'``
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(default ``'mean'``), then:
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.. math::
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\ell(x, y) =
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\begin{cases}
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\operatorname{mean}(L), & \text{if reduction} = \text{`mean';}\\
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\operatorname{sum}(L), & \text{if reduction} = \text{`sum'.}
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\end{cases}
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:math:`x` and :math:`y` are tensors of arbitrary shapes with a total
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of :math:`n` elements each.
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The sum operation still operates over all the elements, and divides by :math:`n`.
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The division by :math:`n` can be avoided if one sets ``reduction = 'sum'``.
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"""
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def __init__(self, p=2, reduction = 'mean', relative = False):
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"""
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:param int p: Degree of Lp norm. It specifies the type of norm to
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be calculated. See :meth:`torch.linalg.norm` ```'ord'``` to
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see the possible degrees. Default 2 (euclidean norm).
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:param str reduction: Specifies the reduction to apply to the output:
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``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction
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will be applied, ``'mean'``: the sum of the output will be divided
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by the number of elements in the output, ``'sum'``: the output will
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be summed. Note: :attr:`size_average` and :attr:`reduce` are in the
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process of being deprecated, and in the meantime, specifying either of
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those two args will override :attr:`reduction`. Default: ``'mean'``.
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:param bool relative: Specifies if relative error should be computed.
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"""
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super().__init__(reduction=reduction)
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# check consistency
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check_consistency(p, (str,int,float), 'degree p')
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self.p = p
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check_consistency(relative, bool, 'relative')
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self.relative = relative
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def forward(self, input, target):
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"""Forward method for loss function.
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:param torch.Tensor input: Input tensor from real data.
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:param torch.Tensor target: Model tensor output.
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:return: Loss evaluation.
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:rtype: torch.Tensor
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"""
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loss = torch.linalg.norm((input-target), ord=self.p, dim=-1)
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if self.relative:
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loss = loss / torch.linalg.norm(input, ord=self.p, dim=-1)
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return self._reduction(loss)
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