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* Adding black as dev dependency
* Formatting pina code
* Formatting tests
This commit is contained in:
Dario Coscia
2025-02-24 11:26:49 +01:00
committed by Nicola Demo
parent 4c4482b155
commit 42ab1a666b
77 changed files with 1170 additions and 924 deletions
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4
pina/model/__init__.py
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@@ -10,7 +10,7 @@ __all__ = [
"AveragingNeuralOperator",
"LowRankNeuralOperator",
"Spline",
"GraphNeuralOperator"
"GraphNeuralOperator",
]
from .feed_forward import FeedForward, ResidualFeedForward
@@ -21,4 +21,4 @@ from .kernel_neural_operator import KernelNeuralOperator
from .average_neural_operator import AveragingNeuralOperator
from .low_rank_neural_operator import LowRankNeuralOperator
from .spline import Spline
from .graph_neural_operator import GraphNeuralOperator
from .graph_neural_operator import GraphNeuralOperator

2
pina/model/block/__init__.py
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@@ -15,7 +15,7 @@ __all__ = [
"AVNOBlock",
"LowRankBlock",
"RBFBlock",
"GNOBlock"
"GNOBlock",
]
from .convolution_2d import ContinuousConvBlock

2
pina/model/block/average_neural_operator_block.py
View File

@@ -1,4 +1,4 @@
""" Module for Averaging Neural Operator Layer class. """
"""Module for Averaging Neural Operator Layer class."""
from torch import nn, mean
from ...utils import check_consistency

2
pina/model/block/embedding.py
View File

@@ -1,4 +1,4 @@
""" Embedding modulus. """
"""Embedding modulus."""
import torch
from pina.utils import check_consistency

39
pina/model/block/gno_block.py
View File

@@ -8,14 +8,14 @@ class GNOBlock(MessagePassing):
"""
def __init__(
self,
width,
edges_features,
n_layers=2,
layers=None,
inner_size=None,
internal_func=None,
external_func=None
self,
width,
edges_features,
n_layers=2,
layers=None,
inner_size=None,
internal_func=None,
external_func=None,
):
"""
Initialize the Graph Integral Layer, inheriting from the MessagePassing class of PyTorch Geometric.
@@ -28,16 +28,19 @@ class GNOBlock(MessagePassing):
:type n_layers: int
"""
from pina.model import FeedForward
super(GNOBlock, self).__init__(aggr='mean')
super(GNOBlock, self).__init__(aggr="mean")
self.width = width
if layers is None and inner_size is None:
inner_size = width
self.dense = FeedForward(input_dimensions=edges_features,
output_dimensions=width ** 2,
n_layers=n_layers,
layers=layers,
inner_size=inner_size,
func=internal_func)
self.dense = FeedForward(
input_dimensions=edges_features,
output_dimensions=width**2,
n_layers=n_layers,
layers=layers,
inner_size=inner_size,
func=internal_func,
)
self.W = torch.nn.Linear(width, width)
self.func = external_func()
@@ -53,7 +56,7 @@ class GNOBlock(MessagePassing):
:rtype: torch.Tensor
"""
x = self.dense(edge_attr).view(-1, self.width, self.width)
return torch.einsum('bij,bj->bi', x, x_j)
return torch.einsum("bij,bj->bi", x, x_j)
def update(self, aggr_out, x):
"""
@@ -82,6 +85,4 @@ class GNOBlock(MessagePassing):
:return: Output of a single iteration over the Graph Integral Layer.
:rtype: torch.Tensor
"""
return self.func(
self.propagate(edge_index, x=x, edge_attr=edge_attr)
)
return self.func(self.propagate(edge_index, x=x, edge_attr=edge_attr))

2
pina/model/block/low_rank_block.py
View File

@@ -1,4 +1,4 @@
""" Module for Averaging Neural Operator Layer class. """
"""Module for Averaging Neural Operator Layer class."""
import torch

71
pina/model/graph_neural_operator.py
View File

@@ -10,16 +10,16 @@ class GraphNeuralKernel(torch.nn.Module):
"""
def __init__(
self,
width,
edge_features,
n_layers=2,
internal_n_layers=0,
internal_layers=None,
inner_size=None,
internal_func=None,
external_func=None,
shared_weights=False
self,
width,
edge_features,
n_layers=2,
internal_n_layers=0,
internal_layers=None,
inner_size=None,
internal_func=None,
external_func=None,
shared_weights=False,
):
"""
The Graph Neural Kernel constructor.
@@ -53,21 +53,24 @@ class GraphNeuralKernel(torch.nn.Module):
layers=internal_layers,
inner_size=inner_size,
internal_func=internal_func,
external_func=external_func)
external_func=external_func,
)
self.n_layers = n_layers
self.forward = self.forward_shared
else:
self.layers = torch.nn.ModuleList(
[GNOBlock(
width=width,
edges_features=edge_features,
n_layers=internal_n_layers,
layers=internal_layers,
inner_size=inner_size,
internal_func=internal_func,
external_func=external_func
)
for _ in range(n_layers)]
[
GNOBlock(
width=width,
edges_features=edge_features,
n_layers=internal_n_layers,
layers=internal_layers,
inner_size=inner_size,
internal_func=internal_func,
external_func=external_func,
)
for _ in range(n_layers)
]
)
def forward(self, x, edge_index, edge_attr):
@@ -107,17 +110,17 @@ class GraphNeuralOperator(KernelNeuralOperator):
"""
def __init__(
self,
lifting_operator,
projection_operator,
edge_features,
n_layers=10,
internal_n_layers=0,
inner_size=None,
internal_layers=None,
internal_func=None,
external_func=None,
shared_weights=True
self,
lifting_operator,
projection_operator,
edge_features,
n_layers=10,
internal_n_layers=0,
inner_size=None,
internal_layers=None,
internal_func=None,
external_func=None,
shared_weights=True,
):
"""
The Graph Neural Operator constructor.
@@ -158,9 +161,9 @@ class GraphNeuralOperator(KernelNeuralOperator):
external_func=external_func,
internal_func=internal_func,
n_layers=n_layers,
shared_weights=shared_weights
shared_weights=shared_weights,
),
projection_operator=projection_operator
projection_operator=projection_operator,
)
def forward(self, x):

7
pina/model/layers/__init__.py
View File

@@ -8,6 +8,7 @@ from ...utils import custom_warning_format
warnings.formatwarning = custom_warning_format
warnings.filterwarnings("always", category=DeprecationWarning)
warnings.warn(
f"'pina.model.layers' is deprecated and will be removed "
f"in future versions. Please use 'pina.model.block' instead.",
DeprecationWarning)
f"'pina.model.layers' is deprecated and will be removed "
f"in future versions. Please use 'pina.model.block' instead.",
DeprecationWarning,
)