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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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88
tests/test_blocks/test_convolution.py
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@@ -18,8 +18,8 @@ def make_grid(x):
# initializing transfomed image
coordinates = torch.zeros(
[channels, prod(dimension),
len(dimension) + 1]).to(image.device)
[channels, prod(dimension), len(dimension) + 1]
).to(image.device)
# creating the n dimensional mesh grid
values_mesh = [
@@ -43,9 +43,13 @@ class MLP(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.model = torch.nn.Sequential(torch.nn.Linear(2, 8), torch.nn.ReLU(),
torch.nn.Linear(8, 8), torch.nn.ReLU(),
torch.nn.Linear(8, 1))
self.model = torch.nn.Sequential(
torch.nn.Linear(2, 8),
torch.nn.ReLU(),
torch.nn.Linear(8, 8),
torch.nn.ReLU(),
torch.nn.Linear(8, 1),
)
def forward(self, x):
return self.model(x)
@@ -61,7 +65,7 @@ stride = {
"domain": [10, 10],
"start": [0, 0],
"jumps": [3, 3],
"direction": [1, 1.]
"direction": [1, 1.0],
}
dim_filter = len(dim)
dim_input = (batch, channel_input, 10, dim_filter)
@@ -73,53 +77,42 @@ x = make_grid(x)
def test_constructor():
model = MLP
conv = ContinuousConvBlock(channel_input,
channel_output,
dim,
stride,
model=model)
conv = ContinuousConvBlock(channel_input,
channel_output,
dim,
stride,
model=None)
conv = ContinuousConvBlock(
channel_input, channel_output, dim, stride, model=model
)
conv = ContinuousConvBlock(
channel_input, channel_output, dim, stride, model=None
)
def test_forward():
model = MLP
# simple forward
conv = ContinuousConvBlock(channel_input,
channel_output,
dim,
stride,
model=model)
conv = ContinuousConvBlock(
channel_input, channel_output, dim, stride, model=model
)
conv(x)
# simple forward with optimization
conv = ContinuousConvBlock(channel_input,
channel_output,
dim,
stride,
model=model,
optimize=True)
conv = ContinuousConvBlock(
channel_input, channel_output, dim, stride, model=model, optimize=True
)
conv(x)
def test_backward():
model = MLP
x = torch.rand(dim_input)
x = make_grid(x)
x.requires_grad = True
# simple backward
conv = ContinuousConvBlock(channel_input,
channel_output,
dim,
stride,
model=model)
conv = ContinuousConvBlock(
channel_input, channel_output, dim, stride, model=model
)
conv(x)
l=torch.mean(conv(x))
l = torch.mean(conv(x))
l.backward()
assert x._grad.shape == torch.Size([2, 2, 20, 3])
x = torch.rand(dim_input)
@@ -127,14 +120,11 @@ def test_backward():
x.requires_grad = True
# simple backward with optimization
conv = ContinuousConvBlock(channel_input,
channel_output,
dim,
stride,
model=model,
optimize=True)
conv = ContinuousConvBlock(
channel_input, channel_output, dim, stride, model=model, optimize=True
)
conv(x)
l=torch.mean(conv(x))
l = torch.mean(conv(x))
l.backward()
assert x._grad.shape == torch.Size([2, 2, 20, 3])
@@ -143,17 +133,13 @@ def test_transpose():
model = MLP
# simple transpose
conv = ContinuousConvBlock(channel_input,
channel_output,
dim,
stride,
model=model)
conv = ContinuousConvBlock(
channel_input, channel_output, dim, stride, model=model
)
conv2 = ContinuousConvBlock(channel_output,
channel_input,
dim,
stride,
model=model)
conv2 = ContinuousConvBlock(
channel_output, channel_input, dim, stride, model=model
)
integrals = conv(x)
conv2.transpose(integrals[..., -1], x)