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Fix rendering and codacy

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This commit is contained in:
FilippoOlivo
2025-03-14 15:05:16 +01:00
committed by Nicola Demo
parent 05105dd517
commit 001d1fc9cf
8 changed files with 98 additions and 96 deletions
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2
pina/model/feed_forward.py
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@@ -154,7 +154,7 @@ class ResidualFeedForward(torch.nn.Module):
:param transformer_nets: The two :class:`torch.nn.Module` acting as
transformer network. The input dimension of both networks must be
equal to ``input_dimensions``, and the output dimension must be
equal to ``inner_size``. If ``None``, two
equal to ``inner_size``. If ``None``, two
:class:`~pina.model.block.residual.EnhancedLinear` layers are used.
Default is ``None``.
:type transformer_nets: list[torch.nn.Module] | tuple[torch.nn.Module]

34
pina/model/fourier_neural_operator.py
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@@ -15,7 +15,7 @@ class FourierIntegralKernel(torch.nn.Module):
"""
Fourier Integral Kernel model class.
This class implements the Fourier Integral Kernel network, which
This class implements the Fourier Integral Kernel network, which
performs global convolution in the Fourier space.
.. seealso::
@@ -109,9 +109,7 @@ class FourierIntegralKernel(torch.nn.Module):
if all(isinstance(i, list) for i in n_modes) and len(layers) != len(
n_modes
):
raise RuntimeError(
"Inconsistent number of layers and modes."
)
raise RuntimeError("Inconsistent number of layers and modes.")
if all(isinstance(i, int) for i in n_modes):
n_modes = [n_modes] * len(layers)
else:
@@ -322,22 +320,22 @@ class FNO(KernelNeuralOperator):
def forward(self, x):
"""
Forward pass for the :class:`FourierNeuralOperator` model.
Forward pass for the :class:`FourierNeuralOperator` model.
The ``lifting_net`` maps the input to the hidden dimension.
Then, several layers of Fourier blocks are applied. Finally, the
``projection_net`` maps the hidden representation to the output
function.
The ``lifting_net`` maps the input to the hidden dimension.
Then, several layers of Fourier blocks are applied. Finally, the
``projection_net`` maps the hidden representation to the output
function.
: param x: The input tensor for performing the computation. Depending
on the ``dimensions`` in the initialization, it expects a tensor
with the following shapes:
* 1D tensors: ``[batch, X, channels]``
* 2D tensors: ``[batch, X, Y, channels]``
* 3D tensors: ``[batch, X, Y, Z, channels]``
:type x: torch.Tensor | LabelTensor
:return: The output tensor.
:rtype: torch.Tensor
: param x: The input tensor for performing the computation. Depending
on the ``dimensions`` in the initialization, it expects a tensor
with the following shapes:
* 1D tensors: ``[batch, X, channels]``
* 2D tensors: ``[batch, X, Y, channels]``
* 3D tensors: ``[batch, X, Y, Z, channels]``
:type x: torch.Tensor | LabelTensor
:return: The output tensor.
:rtype: torch.Tensor
"""
if isinstance(x, LabelTensor):

6
pina/model/kernel_neural_operator.py
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@@ -10,9 +10,9 @@ class KernelNeuralOperator(torch.nn.Module):
r"""
Base class for Neural Operators with integral kernels.
This class serves as a foundation for building Neural Operators that
incorporate multiple integral kernels. All Neural Operator models in
PINA inherit from this class. The design follows the framework proposed
This class serves as a foundation for building Neural Operators that
incorporate multiple integral kernels. All Neural Operator models in
PINA inherit from this class. The design follows the framework proposed
by Kovachki et al., as illustrated in Figure 2 of their work.
Neural Operators derived from this class can be expressed as: