refact

pina/loss/lp_loss.py

@@ -0,0 +1,78 @@
+""" Module for LpLoss class """
+
+import torch
+
+from ..utils import check_consistency
+from .loss_interface import LossInterface
+
+class LpLoss(LossInterface):
+    r"""
+    The Lp loss implementation class. Creates a criterion that measures
+    the Lp error between each element in the input :math:`x` and
+    target :math:`y`.
+
+    The unreduced (i.e. with ``reduction`` set to ``none``) loss can
+    be described as:
+
+    .. math::
+        \ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad
+        l_n = \left[\sum_{i=1}^{D} \left| x_n^i - y_n^i \right|^p \right],
+    
+    If ``'relative'`` is set to true:
+
+    .. 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:
+
+    .. math::
+        \ell(x, y) =
+        \begin{cases}
+            \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
+            for possible degrees. Default 2 (euclidean norm).
+        :param str reduction: Specifies the reduction to apply to the output:
+            ``none`` | ``mean`` | ``sum``. ``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.
+        """
+        super().__init__(reduction=reduction)
+
+        # check consistency
+        check_consistency(p, (str, int, float))
+        check_consistency(relative, bool)
+
+        self.p = p
+        self.relative = relative
+
+    def forward(self, input, target):
+        """Forward method for loss function.
+
+        :param torch.Tensor input: Input tensor from real data.
+        :param torch.Tensor target: Model tensor output.
+        :return: Loss evaluation.
+        :rtype: torch.Tensor
+        """
+        loss = torch.linalg.norm((input - target), ord=self.p, dim=-1)
+        if self.relative:
+            loss = loss / torch.linalg.norm(input, ord=self.p, dim=-1)
+        return self._reduction(loss)