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Fix Codacy Warnings (#477)

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Co-authored-by: Dario Coscia <dariocos99@gmail.com>
This commit is contained in:
Filippo Olivo
2025-03-10 15:38:45 +01:00
committed by Nicola Demo
parent e3790e049a
commit 4177bfbb50
157 changed files with 3473 additions and 3839 deletions
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72
pina/model/graph_neural_operator.py
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@@ -1,6 +1,10 @@
"""
Module for the Graph Neural Operator and Graph Neural Kernel.
"""
import torch
from torch.nn import Tanh
from .block import GNOBlock
from .block.gno_block import GNOBlock
from .kernel_neural_operator import KernelNeuralOperator
@@ -30,14 +34,20 @@ class GraphNeuralKernel(torch.nn.Module):
:type edge_features: int
:param n_layers: The number of kernel layers.
:type n_layers: int
:param internal_n_layers: The number of layers the FF Neural Network internal to each Kernel Layer.
:param internal_n_layers: The number of layers the FF Neural Network
internal to each Kernel Layer.
:type internal_n_layers: int
:param internal_layers: Number of neurons of hidden layers(s) in the FF Neural Network inside for each Kernel Layer.
:param internal_layers: Number of neurons of hidden layers(s) in the
FF Neural Network inside for each Kernel Layer.
:type internal_layers: list | tuple
:param internal_func: The activation function used inside the computation of the representation of the edge features in the Graph Integral Layer.
:param external_func: The activation function applied to the output of the Graph Integral Layer.
:param internal_func: The activation function used inside the
computation of the representation of the edge features in the
Graph Integral Layer.
:param external_func: The activation function applied to the output of
the Graph Integral Layer.
:type external_func: torch.nn.Module
:param shared_weights: If ``True`` the weights of the Graph Integral Layers are shared.
:param shared_weights: If ``True`` the weights of the Graph Integral
Layers are shared.
"""
super().__init__()
if external_func is None:
@@ -56,7 +66,7 @@ class GraphNeuralKernel(torch.nn.Module):
external_func=external_func,
)
self.n_layers = n_layers
self.forward = self.forward_shared
self._forward_func = self._forward_shared
else:
self.layers = torch.nn.ModuleList(
[
@@ -72,25 +82,21 @@ class GraphNeuralKernel(torch.nn.Module):
for _ in range(n_layers)
]
)
self._forward_func = self._forward_unshared
def forward(self, x, edge_index, edge_attr):
"""
The forward pass of the Graph Neural Kernel used when the weights are not shared.
:param x: The input batch.
:type x: torch.Tensor
:param edge_index: The edge index.
:type edge_index: torch.Tensor
:param edge_attr: The edge attributes.
:type edge_attr: torch.Tensor
"""
def _forward_unshared(self, x, edge_index, edge_attr):
for layer in self.layers:
x = layer(x, edge_index, edge_attr)
return x
def forward_shared(self, x, edge_index, edge_attr):
def _forward_shared(self, x, edge_index, edge_attr):
for _ in range(self.n_layers):
x = self.layers(x, edge_index, edge_attr)
return x
def forward(self, x, edge_index, edge_attr):
"""
The forward pass of the Graph Neural Kernel used when the weights are shared.
The forward pass of the Graph Neural Kernel.
:param x: The input batch.
:type x: torch.Tensor
@@ -99,9 +105,7 @@ class GraphNeuralKernel(torch.nn.Module):
:param edge_attr: The edge attributes.
:type edge_attr: torch.Tensor
"""
for _ in range(self.n_layers):
x = self.layers(x, edge_index, edge_attr)
return x
return self._forward_func(x, edge_index, edge_attr)
class GraphNeuralOperator(KernelNeuralOperator):
@@ -125,23 +129,31 @@ class GraphNeuralOperator(KernelNeuralOperator):
"""
The Graph Neural Operator constructor.
:param lifting_operator: The lifting operator mapping the node features to its hidden dimension.
:param lifting_operator: The lifting operator mapping the node features
to its hidden dimension.
:type lifting_operator: torch.nn.Module
:param projection_operator: The projection operator mapping the hidden representation of the nodes features to the output function.
:param projection_operator: The projection operator mapping the hidden
representation of the nodes features to the output function.
:type projection_operator: torch.nn.Module
:param edge_features: Number of edge features.
:type edge_features: int
:param n_layers: The number of kernel layers.
:type n_layers: int
:param internal_n_layers: The number of layers the Feed Forward Neural Network internal to each Kernel Layer.
:param internal_n_layers: The number of layers the Feed Forward Neural
Network internal to each Kernel Layer.
:type internal_n_layers: int
:param internal_layers: Number of neurons of hidden layers(s) in the FF Neural Network inside for each Kernel Layer.
:param internal_layers: Number of neurons of hidden layers(s) in the
FF Neural Network inside for each Kernel Layer.
:type internal_layers: list | tuple
:param internal_func: The activation function used inside the computation of the representation of the edge features in the Graph Integral Layer.
:param internal_func: The activation function used inside the
computation of the representation of the edge features in the
Graph Integral Layer.
:type internal_func: torch.nn.Module
:param external_func: The activation function applied to the output of the Graph Integral Kernel.
:param external_func: The activation function applied to the output of
the Graph Integral Kernel.
:type external_func: torch.nn.Module
:param shared_weights: If ``True`` the weights of the Graph Integral Layers are shared.
:param shared_weights: If ``True`` the weights of the Graph Integral
Layers are shared.
:type shared_weights: bool
"""