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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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54
pina/model/block/convolution_2d.py
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@@ -1,9 +1,9 @@
"""Module for Continuous Convolution class"""
import torch
from .convolution import BaseContinuousConv
from .utils_convolution import check_point, map_points_
from .integral import Integral
import torch
class ContinuousConvBlock(BaseContinuousConv):
@@ -27,8 +27,9 @@ class ContinuousConvBlock(BaseContinuousConv):
.. seealso::
**Original reference**: Coscia, D., Meneghetti, L., Demo, N. et al.
*A continuous convolutional trainable filter for modelling unstructured data*.
Comput Mech 72, 253–265 (2023). DOI `<https://doi.org/10.1007/s00466-023-02291-1>`_
*A continuous convolutional trainable filter for modelling
unstructured data*. Comput Mech 72, 253–265 (2023).
DOI `<https://doi.org/10.1007/s00466-023-02291-1>`_
"""
@@ -45,7 +46,8 @@ class ContinuousConvBlock(BaseContinuousConv):
"""
:param input_numb_field: Number of fields :math:`N_{in}` in the input.
:type input_numb_field: int
:param output_numb_field: Number of fields :math:`N_{out}` in the output.
:param output_numb_field: Number of fields :math:`N_{out}` in the
output.
:type output_numb_field: int
:param filter_dim: Dimension of the filter.
:type filter_dim: tuple(int) | list(int)
@@ -134,6 +136,11 @@ class ContinuousConvBlock(BaseContinuousConv):
# stride for continuous convolution overridden
self._stride = self._stride._stride_discrete
# Define variables
self._index = None
self._grid = None
self._grid_transpose = None
def _spawn_networks(self, model):
"""
Private method to create a collection of kernels
@@ -152,7 +159,7 @@ class ContinuousConvBlock(BaseContinuousConv):
else:
if not isinstance(model, object):
raise ValueError(
"Expected a python class inheriting" " from torch.nn.Module"
"Expected a python class inheriting from torch.nn.Module"
)
for _ in range(self._input_numb_field * self._output_numb_field):
@@ -271,7 +278,7 @@ class ContinuousConvBlock(BaseContinuousConv):
# save on tmp
self._grid_transpose = tmp
def _make_grid(self, X, type):
def _make_grid(self, X, type_):
"""
Private method to create convolution grid.
@@ -283,14 +290,15 @@ class ContinuousConvBlock(BaseContinuousConv):
"""
# choose the type of convolution
if type == "forward":
return self._make_grid_forward(X)
elif type == "inverse":
if type_ == "forward":
self._make_grid_forward(X)
return
if type_ == "inverse":
self._make_grid_transpose(X)
else:
raise TypeError
return
raise TypeError
def _initialize_convolution(self, X, type="forward"):
def _initialize_convolution(self, X, type_="forward"):
"""
Private method to intialize the convolution.
The convolution is initialized by setting a grid and
@@ -304,7 +312,7 @@ class ContinuousConvBlock(BaseContinuousConv):
"""
# variable for the convolution
self._make_grid(X, type)
self._make_grid(X, type_)
# calculate the index
self._find_index(X)
@@ -321,7 +329,7 @@ class ContinuousConvBlock(BaseContinuousConv):
# initialize convolution
if self.training: # we choose what to do based on optimization
self._choose_initialization(X, type="forward")
self._choose_initialization(X, type_="forward")
else: # we always initialize on testing
self._initialize_convolution(X, "forward")
@@ -383,12 +391,14 @@ class ContinuousConvBlock(BaseContinuousConv):
:type integral: torch.tensor
:param X: Input data. Expect tensor of shape
:math:`[B, N_{in}, M, D]` where :math:`B` is the batch_size,
:math`N_{in}`is the number of input fields, :math:`M` the number of points
:math`N_{in}`is the number of input fields, :math:`M` the number of
points
in the mesh, :math:`D` the dimension of the problem.
:type X: torch.Tensor
:return: Feed forward transpose convolution. Tensor of shape
:math:`[B, N_{out}, M, D]` where :math:`B` is the batch_size,
:math`N_{out}`is the number of input fields, :math:`M` the number of points
:math`N_{out}`is the number of input fields, :math:`M` the number of
points
in the mesh, :math:`D` the dimension of the problem.
:rtype: torch.Tensor
@@ -399,7 +409,7 @@ class ContinuousConvBlock(BaseContinuousConv):
# initialize convolution
if self.training: # we choose what to do based on optimization
self._choose_initialization(X, type="inverse")
self._choose_initialization(X, type_="inverse")
else: # we always initialize on testing
self._initialize_convolution(X, "inverse")
@@ -466,12 +476,14 @@ class ContinuousConvBlock(BaseContinuousConv):
:type integral: torch.tensor
:param X: Input data. Expect tensor of shape
:math:`[B, N_{in}, M, D]` where :math:`B` is the batch_size,
:math`N_{in}`is the number of input fields, :math:`M` the number of points
:math`N_{in}`is the number of input fields, :math:`M` the number of
points
in the mesh, :math:`D` the dimension of the problem.
:type X: torch.Tensor
:return: Feed forward transpose convolution. Tensor of shape
:math:`[B, N_{out}, M, D]` where :math:`B` is the batch_size,
:math`N_{out}`is the number of input fields, :math:`M` the number of points
:math`N_{out}`is the number of input fields, :math:`M` the number of
points
in the mesh, :math:`D` the dimension of the problem.
:rtype: torch.Tensor
@@ -481,7 +493,7 @@ class ContinuousConvBlock(BaseContinuousConv):
# initialize convolution
if self.training: # we choose what to do based on optimization
self._choose_initialization(X, type="inverse")
self._choose_initialization(X, type_="inverse")
else: # we always initialize on testing
self._initialize_convolution(X, "inverse")
@@ -491,7 +503,7 @@ class ContinuousConvBlock(BaseContinuousConv):
conv_transposed = self._grid_transpose.clone().detach()
# list to iterate for calculating nn output
tmp = [i for i in range(self._output_numb_field)]
tmp = list(range(self._output_numb_field))
iterate_conv = [
item for item in tmp for _ in range(self._input_numb_field)
]