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fix doc loss and codacy

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giovanni
2025-03-12 18:05:42 +01:00
committed by FilippoOlivo
parent 2c9e980c7f
commit da1ac90b99
15 changed files with 114 additions and 108 deletions
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50
pina/loss/power_loss.py
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@@ -8,27 +8,27 @@ from .loss_interface import LossInterface
class PowerLoss(LossInterface):
r"""
The PowerLoss loss implementation class. Creates a criterion that measures
the error between each element in the input :math:`x` and
target :math:`y` powered to a specific integer.
Implementation of the Power Loss. It defines a criterion to measures the
pointwise error between values in the input :math:`x` and values in the
target :math:`y`.
The unreduced (i.e. with ``reduction`` set to ``none``) loss can
be described as:
If ``reduction`` is set to ``none``, the loss can be written as:
.. math::
\ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad
l_n = \frac{1}{D}\left[\sum_{i=1}^{D}
\left| x_n^i - y_n^i \right|^p\right],
If ``'relative'`` is set to true:
If ``relative`` is set to ``True``, the relative error is computed:
.. math::
\ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad
l_n = \frac{ \sum_{i=1}^{D} | x_n^i - y_n^i|^p }
{\sum_{i=1}^{D}|y_n^i|^p},
where :math:`N` is the batch size. If ``reduction`` is not ``none``
(default ``mean``), then:
where :math:`N` is the batch size.
If ``reduction`` is not ``none``, then:
.. math::
\ell(x, y) =
@@ -36,30 +36,21 @@ class PowerLoss(LossInterface):
\operatorname{mean}(L), & \text{if reduction} = \text{`mean';}\\
\operatorname{sum}(L), & \text{if reduction} = \text{`sum'.}
\end{cases}
:math:`x` and :math:`y` are tensors of arbitrary shapes with a total
of :math:`n` elements each.
The sum operation still operates over all the elements, and divides by
:math:`n`.
The division by :math:`n` can be avoided if one sets ``reduction`` to
``sum``.
"""
def __init__(self, p=2, reduction="mean", relative=False):
"""
:param int p: Degree of Lp norm. It specifies the type of norm to
be calculated. See `list of possible orders in torch linalg
<https://pytorch.org/docs/stable/generated/
torch.linalg.norm.html#torch.linalg.norm>`_ to
see the possible degrees. Default 2 (euclidean norm).
:param str reduction: Specifies the reduction to apply to the output:
``none`` | ``mean`` | ``sum``. When ``none``: no reduction
will be applied, ``mean``: the sum of the output will be divided
by the number of elements in the output, ``sum``: the output will
be summed.
:param bool relative: Specifies if relative error should be computed.
Initialization of the :class:`PowerLoss` class.
:param int p: Degree of the Lp norm. It specifies the norm to be
computed. Default is ``2`` (euclidean norm).
:param str reduction: The reduction method for the loss.
Available options: ``none``, ``mean``, ``sum``.
If ``none``, no reduction is applied. If ``mean``, the sum of the
loss values is divided by the number of values. If ``sum``, the loss
values are summed. Default is ``mean``.
:param bool relative: If ``True``, the relative error is computed.
Default is ``False``.
"""
super().__init__(reduction=reduction)
@@ -71,7 +62,8 @@ class PowerLoss(LossInterface):
self.relative = relative
def forward(self, input, target):
"""Forward method for loss function.
"""
Forward method of the loss function.
:param torch.Tensor input: Input tensor from real data.
:param torch.Tensor target: Model tensor output.