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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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123
pina/model/deeponet.py
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@@ -1,9 +1,9 @@
"""Module for DeepONet model"""
import torch
import torch.nn as nn
from ..utils import check_consistency, is_function
from functools import partial
import torch
from torch import nn
from ..utils import check_consistency, is_function
class MIONet(torch.nn.Module):
@@ -12,8 +12,9 @@ class MIONet(torch.nn.Module):
MIONet is a general architecture for learning Operators defined
on the tensor product of Banach spaces. Unlike traditional machine
learning methods MIONet is designed to map entire functions to other functions.
It can be trained both with Physics Informed or Supervised learning strategies.
learning methods MIONet is designed to map entire functions to other
functions. It can be trained both with Physics Informed or Supervised
learning strategies.
.. seealso::
@@ -37,37 +38,45 @@ class MIONet(torch.nn.Module):
:param dict networks: The neural networks to use as
models. The ``dict`` takes as key a neural network, and
as value the list of indeces to extract from the input variable
in the forward pass of the neural network. If a list of ``int`` is passed,
the corresponding columns of the inner most entries are extracted.
If a list of ``str`` is passed the variables of the corresponding :py:obj:`pina.label_tensor.LabelTensor`
are extracted. The ``torch.nn.Module`` model has to take as input a
in the forward pass of the neural network. If a list of ``int``
is passed, the corresponding columns of the inner most entries are
extracted.
If a list of ``str`` is passed the variables of the corresponding
:py:obj:`pina.label_tensor.LabelTensor`are extracted. The
``torch.nn.Module`` model has to take as input a
:py:obj:`pina.label_tensor.LabelTensor` or :class:`torch.Tensor`.
Default implementation consist of different branch nets and one trunk nets.
Default implementation consist of different branch nets and one
trunk nets.
:param str or Callable aggregator: Aggregator to be used to aggregate
partial results from the modules in `nets`. Partial results are
aggregated component-wise. Available aggregators include
sum: ``+``, product: ``*``, mean: ``mean``, min: ``min``, max: ``max``.
sum: ``+``, product: ``*``, mean: ``mean``, min: ``min``, max:
``max``.
:param str or Callable reduction: Reduction to be used to reduce
the aggregated result of the modules in `nets` to the desired output
dimension. Available reductions include
sum: ``+``, product: ``*``, mean: ``mean``, min: ``min``, max: ``max``.
:param bool or Callable scale: Scaling the final output before returning the
forward pass, default ``True``.
sum: ``+``, product: ``*``, mean: ``mean``, min: ``min``, max:
``max``.
:param bool or Callable scale: Scaling the final output before returning
the forward pass, default ``True``.
:param bool or Callable translation: Translating the final output before
returning the forward pass, default ``True``.
.. warning::
In the forward pass we do not check if the input is instance of
:py:obj:`pina.label_tensor.LabelTensor` or :class:`torch.Tensor`. A general rule is
that for a :py:obj:`pina.label_tensor.LabelTensor` input both list of integers and
list of strings can be passed for ``input_indeces_branch_net``
and ``input_indeces_trunk_net``. Differently, for a :class:`torch.Tensor`
only a list of integers can be passed for ``input_indeces_branch_net``
and ``input_indeces_trunk_net``.
:py:obj:`pina.label_tensor.LabelTensor` or :class:`torch.Tensor`.
A general rule is that for a :py:obj:`pina.label_tensor.LabelTensor`
input both list of integers and list of strings can be passed for
``input_indeces_branch_net``and ``input_indeces_trunk_net``.
Differently, for a :class:`torch.Tensor` only a list of integers
can be passed for ``input_indeces_branch_net``and
``input_indeces_trunk_net``.
:Example:
>>> branch_net1 = FeedForward(input_dimensons=1, output_dimensions=10)
>>> branch_net2 = FeedForward(input_dimensons=2, output_dimensions=10)
>>> branch_net1 = FeedForward(input_dimensons=1,
... output_dimensions=10)
>>> branch_net2 = FeedForward(input_dimensons=2,
... output_dimensions=10)
>>> trunk_net = FeedForward(input_dimensons=1, output_dimensions=10)
>>> networks = {branch_net1 : ['x'],
branch_net2 : ['x', 'y'],
@@ -125,7 +134,7 @@ class MIONet(torch.nn.Module):
if not all(map(lambda x: x == shapes[0], shapes)):
raise ValueError(
"The passed networks have not the same " "output dimension."
"The passed networks have not the same output dimension."
)
# assign trunk and branch net with their input indeces
@@ -163,7 +172,7 @@ class MIONet(torch.nn.Module):
}
def _init_aggregator(self, aggregator):
aggregator_funcs = DeepONet._symbol_functions(dim=2)
aggregator_funcs = self._symbol_functions(dim=2)
if aggregator in aggregator_funcs:
aggregator_func = aggregator_funcs[aggregator]
elif isinstance(aggregator, nn.Module) or is_function(aggregator):
@@ -175,7 +184,7 @@ class MIONet(torch.nn.Module):
self._aggregator_type = aggregator
def _init_reduction(self, reduction):
reduction_funcs = DeepONet._symbol_functions(dim=-1)
reduction_funcs = self._symbol_functions(dim=-1)
if reduction in reduction_funcs:
reduction_func = reduction_funcs[reduction]
elif isinstance(reduction, nn.Module) or is_function(reduction):
@@ -190,13 +199,13 @@ class MIONet(torch.nn.Module):
if isinstance(indeces[0], str):
try:
return x.extract(indeces)
except AttributeError:
except AttributeError as e:
raise RuntimeError(
"Not possible to extract input variables from tensor."
" Ensure that the passed tensor is a LabelTensor or"
" pass list of integers to extract variables. For"
" more information refer to warning in the documentation."
)
) from e
elif isinstance(indeces[0], int):
return x[..., indeces]
else:
@@ -209,7 +218,8 @@ class MIONet(torch.nn.Module):
"""
Defines the computation performed at every call.
:param LabelTensor or torch.Tensor x: The input tensor for the forward call.
:param LabelTensor or torch.Tensor x: The input tensor for the forward
call.
:return: The output computed by the DeepONet model.
:rtype: LabelTensor or torch.Tensor
"""
@@ -225,7 +235,7 @@ class MIONet(torch.nn.Module):
# reduce
output_ = self._reduction(aggregated)
if self._reduction_type in DeepONet._symbol_functions(dim=-1):
if self._reduction_type in self._symbol_functions(dim=-1):
output_ = output_.reshape(-1, 1)
# scale and translate
@@ -309,47 +319,55 @@ class DeepONet(MIONet):
):
"""
:param torch.nn.Module branch_net: The neural network to use as branch
model. It has to take as input a :py:obj:`pina.label_tensor.LabelTensor`
or :class:`torch.Tensor`. The number of dimensions of the output has
to be the same of the ``trunk_net``.
model. It has to take as input a
:py:obj:`pina.label_tensor.LabelTensor` or :class:`torch.Tensor`.
The number of dimensions of the output has to be the same of the
``trunk_net``.
:param torch.nn.Module trunk_net: The neural network to use as trunk
model. It has to take as input a :py:obj:`pina.label_tensor.LabelTensor`
or :class:`torch.Tensor`. The number of dimensions of the output
has to be the same of the ``branch_net``.
model. It has to take as input a
:py:obj:`pina.label_tensor.LabelTensor` or :class:`torch.Tensor`.
The number of dimensions of the output has to be the same of the
``branch_net``.
:param list(int) or list(str) input_indeces_branch_net: List of indeces
to extract from the input variable in the forward pass for the
branch net. If a list of ``int`` is passed, the corresponding columns
of the inner most entries are extracted. If a list of ``str`` is passed
the variables of the corresponding :py:obj:`pina.label_tensor.LabelTensor` are extracted.
branch net. If a list of ``int`` is passed, the corresponding
columns of the inner most entries are extracted. If a list of
``str`` is passed the variables of the corresponding
:py:obj:`pina.label_tensor.LabelTensor` are extracted.
:param list(int) or list(str) input_indeces_trunk_net: List of indeces
to extract from the input variable in the forward pass for the
trunk net. If a list of ``int`` is passed, the corresponding columns
of the inner most entries are extracted. If a list of ``str`` is passed
the variables of the corresponding :py:obj:`pina.label_tensor.LabelTensor` are extracted.
of the inner most entries are extracted. If a list of ``str`` is
passed the variables of the corresponding
:py:obj:`pina.label_tensor.LabelTensor` are extracted.
:param str or Callable aggregator: Aggregator to be used to aggregate
partial results from the modules in `nets`. Partial results are
aggregated component-wise. Available aggregators include
sum: ``+``, product: ``*``, mean: ``mean``, min: ``min``, max: ``max``.
sum: ``+``, product: ``*``, mean: ``mean``, min: ``min``,
max: ``max``.
:param str or Callable reduction: Reduction to be used to reduce
the aggregated result of the modules in `nets` to the desired output
dimension. Available reductions include
sum: ``+``, product: ``*``, mean: ``mean``, min: ``min``, max: ``max``.
:param bool or Callable scale: Scaling the final output before returning the
forward pass, default True.
sum: ``+``, product: ``*``, mean: ``mean``, min: ``min``,
max: ``max``.
:param bool or Callable scale: Scaling the final output before returning
the forward pass, default True.
:param bool or Callable translation: Translating the final output before
returning the forward pass, default True.
.. warning::
In the forward pass we do not check if the input is instance of
:py:obj:`pina.label_tensor.LabelTensor` or :class:`torch.Tensor`. A general rule is
that for a :py:obj:`pina.label_tensor.LabelTensor` input both list of integers and
list of strings can be passed for ``input_indeces_branch_net``
and ``input_indeces_trunk_net``. Differently, for a :class:`torch.Tensor`
only a list of integers can be passed for ``input_indeces_branch_net``
and ``input_indeces_trunk_net``.
:py:obj:`pina.label_tensor.LabelTensor` or :class:`torch.Tensor`.
A general rule is that for a :py:obj:`pina.label_tensor.LabelTensor`
input both list of integers and list of strings can be passed for
``input_indeces_branch_net`` and ``input_indeces_trunk_net``.
Differently, for a :class:`torch.Tensor` only a list of integers can
be passed for ``input_indeces_branch_net`` and
``input_indeces_trunk_net``.
:Example:
>>> branch_net = FeedForward(input_dimensons=1, output_dimensions=10)
>>> branch_net = FeedForward(input_dimensons=1,
... output_dimensions=10)
>>> trunk_net = FeedForward(input_dimensons=1, output_dimensions=10)
>>> model = DeepONet(branch_net=branch_net,
... trunk_net=trunk_net,
@@ -395,7 +413,8 @@ class DeepONet(MIONet):
"""
Defines the computation performed at every call.
:param LabelTensor or torch.Tensor x: The input tensor for the forward call.
:param LabelTensor or torch.Tensor x: The input tensor for the forward
call.
:return: The output computed by the DeepONet model.
:rtype: LabelTensor or torch.Tensor
"""