Fix cyclic imports warning and other minor fixes

Author: FilippoOlivo

pina/__init__.py

@@ -1,3 +1,7 @@
+"""
+Module for the Pina library.
+"""
+
 __all__ = [
     "Trainer",
     "LabelTensor",

pina/adaptive_function/adaptive_function.py

@@ -50,14 +50,14 @@ class AdaptiveSigmoid(AdaptiveActivationFunctionInterface):
     r"""
     Adaptive trainable :class:`~torch.nn.Sigmoid` activation function.
 
-    Given the function 
+    Given the function
     :math:`\text{Sigmoid}:\mathbb{R}^n\rightarrow\mathbb{R}^n`,
     the adaptive function
     :math:`\text{Sigmoid}_{\text{adaptive}}:\mathbb{R}^n\rightarrow\mathbb{R}^n`
     is defined as:
 
     .. math::
-        \text{Sigmoid}_{\text{adaptive}}({x})= \\
+        \text{Sigmoid}_{\text{adaptive}}({x})=
         \alpha\,\text{Sigmoid}(\beta{x}+\gamma),
 
     where :math:`\alpha,\,\beta,\,\gamma` are trainable parameters, and the
@@ -344,15 +344,15 @@ class AdaptiveSoftmin(AdaptiveActivationFunctionInterface):
     r"""
     Adaptive trainable :class:`~torch.nn.Softmin` activation function.
 
-    Given the function 
+    Given the function
     :math:`\text{Softmin}:\mathbb{R}^n\rightarrow\mathbb{R}^n`,
     the adaptive function
     :math:`\text{Softmin}_{\text{adaptive}}:\mathbb{R}^n\rightarrow\mathbb{R}^n`
     is defined as:
 
     .. math::
-        \text{Softmin}_{\text{adaptive}}({x})= \\
-        \alpha\,\text{Softmin}(\beta{x}+\gamma),
+        \text{Softmin}_{\text{adaptive}}({x})=\alpha\,
+        \text{Softmin}(\beta{x}+\gamma),
 
     where :math:`\alpha,\,\beta,\,\gamma` are trainable parameters, and the
     Softmin function is defined as:
@@ -387,15 +387,15 @@ class AdaptiveSoftmax(AdaptiveActivationFunctionInterface):
     r"""
     Adaptive trainable :class:`~torch.nn.Softmax` activation function.
 
-    Given the function 
+    Given the function
     :math:`\text{Softmax}:\mathbb{R}^n\rightarrow\mathbb{R}^n`,
     the adaptive function
     :math:`\text{Softmax}_{\text{adaptive}}:\mathbb{R}^n\rightarrow\mathbb{R}^n`
     is defined as:
 
     .. math::
-        \text{Softmax}_{\text{adaptive}}({x})= \\
-        \alpha\,\text{Softmax}(\beta{x}+\gamma),
+        \text{Softmax}_{\text{adaptive}}({x})=\alpha\,
+        \text{Softmax}(\beta{x}+\gamma),
 
     where :math:`\alpha,\,\beta,\,\gamma` are trainable parameters, and the
     Softmax function is defined as:

pina/domain/exclusion_domain.py

@@ -18,8 +18,8 @@ def __init__(self, geometries):
         domain difference is defined as:
 
         .. math::
-            A \setminus B = \{x \mid x \in A \land x \in B \land  x \not\in \\
-                (A \lor B)\},
+            A \setminus B = \{x \mid x \in A \land x \in B \land
+            x \not\in(A \lor B)\},
 
         with :math:`x` a point in :math:`\mathbb{R}^N` and :math:`N`
         the dimension of the geometry space.

pina/graph.py

@@ -5,7 +5,7 @@
 import torch
 from torch_geometric.data import Data, Batch
 from torch_geometric.utils import to_undirected
-from . import LabelTensor
+from .label_tensor import LabelTensor
 from .utils import check_consistency, is_function
 
 

pina/loss/lp_loss.py

@@ -25,8 +25,8 @@ class LpLoss(LossInterface):
 
     .. 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]},
+        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:

pina/model/block/gno_block.py

@@ -30,7 +30,7 @@ def __init__(
         """
         # Avoid circular import
         # pylint: disable=import-outside-toplevel
-        from ...model import FeedForward
+        from ...model.feed_forward import FeedForward
 
         super().__init__(aggr="mean")  # Uses PyG's default aggregation
         self.width = width

pina/model/graph_neural_operator.py

@@ -4,7 +4,7 @@
 
 import torch
 from torch.nn import Tanh
-from .block import GNOBlock
+from .block.gno_block import GNOBlock
 from .kernel_neural_operator import KernelNeuralOperator
 
 

pina/model/multi_feed_forward.py

@@ -1,11 +1,11 @@
 """Module for Multi FeedForward model"""
 
+from abc import ABC, abstractmethod
 import torch
-
 from .feed_forward import FeedForward
 
 
-class MultiFeedForward(torch.nn.Module):
+class MultiFeedForward(torch.nn.Module, ABC):
     """
     The PINA implementation of MultiFeedForward network.
 
@@ -24,3 +24,9 @@ def __init__(self, ffn_dict):
 
         for name, constructor_args in ffn_dict.items():
             setattr(self, name, FeedForward(**constructor_args))
+
+    @abstractmethod
+    def forward(self, *args, **kwargs):
+        """
+        TODO: Docstring
+        """

pina/problem/abstract_problem.py

@@ -5,7 +5,7 @@
 from ..utils import check_consistency
 from ..domain import DomainInterface, CartesianDomain
 from ..condition.domain_equation_condition import DomainEquationCondition
-from .. import LabelTensor
+from ..label_tensor import LabelTensor
 from ..utils import merge_tensors
 
 

pina/solver/garom.py

@@ -184,7 +184,7 @@ def on_train_batch_end(self, outputs, batch, batch_idx):
         # increase by one the counter of optimization to save loggers
         (
             # Unavoidable long line
-            # pylint: disable=line-too
+            # pylint: disable=line-too-long
             self.trainer.fit_loop.epoch_loop.manual_optimization.optim_step_progress.total.completed
         ) += 1