folivo/PINA
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Fix rendering and codacy

Browse Source
This commit is contained in:
FilippoOlivo
2025-03-14 15:05:16 +01:00
parent 436b227809
commit 194f5d24c4
8 changed files with 98 additions and 96 deletions
Download Patch File Download Diff File Expand all files Collapse all files

34
pina/model/fourier_neural_operator.py
View File

@@ -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):