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

Fix Codacy Warnings (#477)

Browse Source 
---------

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
Download Patch File Download Diff File Expand all files Collapse all files

90
tests/test_blocks/test_spectral_convolution.py
View File

@@ -1,4 +1,8 @@
from pina.model.block import SpectralConvBlock1D, SpectralConvBlock2D, SpectralConvBlock3D
from pina.model.block import (
SpectralConvBlock1D,
SpectralConvBlock2D,
SpectralConvBlock3D,
)
import torch
input_numb_fields = 3
@@ -7,78 +11,96 @@ batch = 5
def test_constructor_1d():
SpectralConvBlock1D(input_numb_fields=input_numb_fields,
output_numb_fields=output_numb_fields,
n_modes=5)
SpectralConvBlock1D(
input_numb_fields=input_numb_fields,
output_numb_fields=output_numb_fields,
n_modes=5,
)
def test_forward_1d():
sconv = SpectralConvBlock1D(input_numb_fields=input_numb_fields,
output_numb_fields=output_numb_fields,
n_modes=4)
sconv = SpectralConvBlock1D(
input_numb_fields=input_numb_fields,
output_numb_fields=output_numb_fields,
n_modes=4,
)
x = torch.rand(batch, input_numb_fields, 10)
sconv(x)
def test_backward_1d():
sconv = SpectralConvBlock1D(input_numb_fields=input_numb_fields,
output_numb_fields=output_numb_fields,
n_modes=4)
sconv = SpectralConvBlock1D(
input_numb_fields=input_numb_fields,
output_numb_fields=output_numb_fields,
n_modes=4,
)
x = torch.rand(batch, input_numb_fields, 10)
x.requires_grad = True
sconv(x)
l=torch.mean(sconv(x))
l = torch.mean(sconv(x))
l.backward()
assert x._grad.shape == torch.Size([5,3,10])
assert x._grad.shape == torch.Size([5, 3, 10])
def test_constructor_2d():
SpectralConvBlock2D(input_numb_fields=input_numb_fields,
output_numb_fields=output_numb_fields,
n_modes=[5, 4])
SpectralConvBlock2D(
input_numb_fields=input_numb_fields,
output_numb_fields=output_numb_fields,
n_modes=[5, 4],
)
def test_forward_2d():
sconv = SpectralConvBlock2D(input_numb_fields=input_numb_fields,
output_numb_fields=output_numb_fields,
n_modes=[5, 4])
sconv = SpectralConvBlock2D(
input_numb_fields=input_numb_fields,
output_numb_fields=output_numb_fields,
n_modes=[5, 4],
)
x = torch.rand(batch, input_numb_fields, 10, 10)
sconv(x)
def test_backward_2d():
sconv = SpectralConvBlock2D(input_numb_fields=input_numb_fields,
output_numb_fields=output_numb_fields,
n_modes=[5, 4])
sconv = SpectralConvBlock2D(
input_numb_fields=input_numb_fields,
output_numb_fields=output_numb_fields,
n_modes=[5, 4],
)
x = torch.rand(batch, input_numb_fields, 10, 10)
x.requires_grad = True
sconv(x)
l=torch.mean(sconv(x))
l = torch.mean(sconv(x))
l.backward()
assert x._grad.shape == torch.Size([5,3,10,10])
assert x._grad.shape == torch.Size([5, 3, 10, 10])
def test_constructor_3d():
SpectralConvBlock3D(input_numb_fields=input_numb_fields,
output_numb_fields=output_numb_fields,
n_modes=[5, 4, 4])
SpectralConvBlock3D(
input_numb_fields=input_numb_fields,
output_numb_fields=output_numb_fields,
n_modes=[5, 4, 4],
)
def test_forward_3d():
sconv = SpectralConvBlock3D(input_numb_fields=input_numb_fields,
output_numb_fields=output_numb_fields,
n_modes=[5, 4, 4])
sconv = SpectralConvBlock3D(
input_numb_fields=input_numb_fields,
output_numb_fields=output_numb_fields,
n_modes=[5, 4, 4],
)
x = torch.rand(batch, input_numb_fields, 10, 10, 10)
sconv(x)
def test_backward_3d():
sconv = SpectralConvBlock3D(input_numb_fields=input_numb_fields,
output_numb_fields=output_numb_fields,
n_modes=[5, 4, 4])
sconv = SpectralConvBlock3D(
input_numb_fields=input_numb_fields,
output_numb_fields=output_numb_fields,
n_modes=[5, 4, 4],
)
x = torch.rand(batch, input_numb_fields, 10, 10, 10)
x.requires_grad = True
sconv(x)
l=torch.mean(sconv(x))
l = torch.mean(sconv(x))
l.backward()
assert x._grad.shape == torch.Size([5,3,10,10,10])
assert x._grad.shape == torch.Size([5, 3, 10, 10, 10])