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test: Fix type hints in architectures.py #426

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ValerianRey merged 1 commit into from
Sep 28, 2025
Merged

test: Fix type hints in architectures.py #426

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58 changes: 29 additions & 29 deletions tests/utils/architectures.py
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Original file line number Diff line number Diff line change
Expand Up @@ -49,7 +49,7 @@ def __init__(self):
nn.Linear(13, 14),
)

def forward(self, input: Tensor):
def forward(self, input: Tensor) -> Tensor:
return self.seq(input)


Expand All @@ -64,7 +64,7 @@ def __init__(self):
self.matrix1 = nn.Parameter(torch.randn(50, 60))
self.matrix2 = nn.Parameter(torch.randn(50, 60))

def forward(self, inputs: tuple[Tensor, Tensor]) -> tuple[Tensor, Tensor]:
def forward(self, inputs: tuple[Tensor, Tensor]) -> Tensor:
input1, input2 = inputs
output = input1 @ self.matrix1 + input2 @ self.matrix2
return output
Expand Down Expand Up @@ -133,7 +133,7 @@ def __init__(self):
self.matrix4 = nn.Parameter(torch.randn(12, 80))
self.matrix5 = nn.Parameter(torch.randn(14, 90))

def forward(self, inputs: PyTree) -> PyTree:
def forward(self, inputs: PyTree) -> Tensor:
input1 = inputs["one"][0][0]
input2 = inputs["one"][0][1][0]
input3 = inputs["one"][0][1][1]
Expand Down Expand Up @@ -259,7 +259,7 @@ def __init__(self):
super().__init__()
self.sipo = SingleInputPyTreeOutput()

def forward(self, input: Tensor):
def forward(self, input: Tensor) -> Tensor:
first, second, third = self.sipo(input).values()
output1, output23 = first
output2, output3 = output23
Expand All @@ -282,7 +282,7 @@ def __init__(self):
super().__init__()
self.piso = PyTreeInputSingleOutput()

def forward(self, input: Tensor):
def forward(self, input: Tensor) -> Tensor:
input1 = input[:, 0:10]
input2 = input[:, 10:30]
input3 = input[:, 30:60]
Expand Down Expand Up @@ -363,7 +363,7 @@ def __init__(self, shape: tuple[int, ...]):
super().__init__()
self.matrix = nn.Parameter(torch.randn(shape))

def forward(self, _: PyTree) -> PyTree:
def forward(self, _: PyTree) -> tuple:
return tuple()

class _EmptyPytreeOutput(nn.Module):
Expand All @@ -382,7 +382,7 @@ def __init__(self):
self.empty_pytree_output = self._EmptyPytreeOutput((27, 10))
self.linear = nn.Linear(27, 10)

def forward(self, input: Tensor):
def forward(self, input: Tensor) -> Tensor:
_ = self.none_output(input)
_ = self.none_pytree_output(input)
_ = self.empty_tuple_output(input)
Expand All @@ -400,7 +400,7 @@ def __init__(self):
super().__init__()
self.matrix = nn.Parameter(torch.randn(50, 10))

def forward(self, input: Tensor):
def forward(self, input: Tensor) -> Tensor:
return input @ self.matrix + (input**2 / 5.0) @ self.matrix


Expand Down Expand Up @@ -429,7 +429,7 @@ def __init__(self):
self.module1 = self._MatMulModule(matrix)
self.module2 = self._MatMulModule(matrix)

def forward(self, input: Tensor):
def forward(self, input: Tensor) -> Tensor:
return self.module1(input) + self.module2(input**2 / 5.0)


Expand All @@ -443,7 +443,7 @@ def __init__(self):
super().__init__()
self.module = nn.Linear(50, 10)

def forward(self, input: Tensor):
def forward(self, input: Tensor) -> Tensor:
return self.module(input) + self.module(input**2 / 5.0)


Expand All @@ -458,7 +458,7 @@ def __init__(self):
self.unused_param = nn.Parameter(torch.randn(50, 10))
self.matrix = nn.Parameter(torch.randn(50, 10))

def forward(self, input: Tensor):
def forward(self, input: Tensor) -> Tensor:
return input @ self.matrix


Expand All @@ -476,7 +476,7 @@ def __init__(self):
self.frozen_param = nn.Parameter(torch.randn(50, 10), requires_grad=False)
self.matrix = nn.Parameter(torch.randn(50, 10))

def forward(self, input: Tensor):
def forward(self, input: Tensor) -> Tensor:
return input @ self.matrix + (input**2 / 5.0) @ self.frozen_param


Expand All @@ -494,7 +494,7 @@ def __init__(self):
self.all_frozen = nn.Linear(50, 10)
self.all_frozen.requires_grad_(False)

def forward(self, input: Tensor):
def forward(self, input: Tensor) -> Tensor:
return self.all_frozen(input) + self.non_frozen(input**2 / 5.0)


Expand Down Expand Up @@ -522,15 +522,15 @@ def __init__(self):
self.frozen_param = nn.Parameter(torch.randn(50, 10), requires_grad=False)
self.non_frozen_param = nn.Parameter(torch.randn(50, 10))

def forward(self, input: Tensor):
def forward(self, input: Tensor) -> Tensor:
return input @ self.frozen_param

def __init__(self):
super().__init__()
self.weird_module = self.SomeFrozenParamAndUnusedTrainableParam()
self.normal_module = nn.Linear(10, 3)

def forward(self, input: Tensor):
def forward(self, input: Tensor) -> Tensor:
return self.normal_module(self.weird_module(input))


Expand All @@ -548,7 +548,7 @@ def __init__(self):
self.frozen_param = nn.Parameter(torch.randn(50, 10), requires_grad=False)
self.matrix = nn.Parameter(torch.randn(50, 10))

def forward(self, input: Tensor):
def forward(self, input: Tensor) -> tuple[Tensor, Tensor]:
return (input**2 / 5.0) @ self.frozen_param, input @ self.matrix


Expand All @@ -563,15 +563,15 @@ def __init__(self):
super().__init__()
self.buffer = nn.Buffer(torch.tensor(1.5))

def forward(self, input: Tensor):
def forward(self, input: Tensor) -> Tensor:
return input * self.buffer

def __init__(self):
super().__init__()
self.module_with_buffer = self._Buffered()
self.linear = nn.Linear(27, 10)

def forward(self, input: Tensor):
def forward(self, input: Tensor) -> Tensor:
return self.linear(self.module_with_buffer(input))


Expand All @@ -585,7 +585,7 @@ def __init__(self):
super().__init__()
self.matrix = nn.Parameter(torch.randn(9, 10))

def forward(self, input: Tensor):
def forward(self, input: Tensor) -> Tensor:
noise = torch.zeros_like(input)
noise.normal_()
return (input * noise) @ self.matrix
Expand All @@ -602,7 +602,7 @@ def __init__(self):
self.matrix = nn.Parameter(torch.randn(9, 10))
self.buffer = nn.Buffer(torch.zeros((9,)))

def forward(self, input: Tensor):
def forward(self, input: Tensor) -> Tensor:
self.buffer = self.buffer + 1.0
return (input + self.buffer) @ self.matrix

Expand All @@ -621,7 +621,7 @@ def __init__(self):
self.linear1 = nn.Linear(9, 12)
self.linear2 = nn.Linear(9, 10)

def forward(self, input: Tensor):
def forward(self, input: Tensor) -> Tensor:
_ = self.linear1(input)
output = self.linear2(input)
return output
Expand Down Expand Up @@ -705,7 +705,7 @@ def __init__(self):
super().__init__()
self.rnn = nn.RNN(input_size=10, hidden_size=5)

def forward(self, input: Tensor) -> Tensor:
def forward(self, input: Tensor) -> None:
pass


Expand Down Expand Up @@ -755,7 +755,7 @@ def __init__(self):
self.linear3 = nn.Linear(60, 70)
self.linear4 = nn.Linear(70, 80)

def forward(self, input: Tensor):
def forward(self, input: Tensor) -> Tensor:
output = self.relu(input @ self.matrix)
output = self.relu(self.linear1(output))
output = self.relu(self.linear2(output))
Expand All @@ -782,7 +782,7 @@ def __init__(self):
self.linear3 = nn.Linear(60, 70)
self.linear4 = nn.Linear(70, 80)

def forward(self, input: Tensor):
def forward(self, input: Tensor) -> Tensor:
output = self.relu(self.linear0(input))
output = self.relu(self.linear1(output))
output = self.relu(self.linear2(output))
Expand Down Expand Up @@ -864,7 +864,7 @@ def __init__(self):
super().__init__()
self.alexnet = torchvision.models.alexnet()

def forward(self, input: Tensor):
def forward(self, input: Tensor) -> Tensor:
return self.alexnet(input)


Expand All @@ -883,7 +883,7 @@ def __init__(self):
norm_layer=partial(nn.InstanceNorm2d, track_running_stats=False, affine=True)
)

def forward(self, input: Tensor):
def forward(self, input: Tensor) -> Tensor:
return self.resnet18(input) / 5.0


Expand All @@ -899,7 +899,7 @@ def __init__(self):
norm_layer=partial(nn.GroupNorm, 2, affine=True)
)

def forward(self, input: Tensor):
def forward(self, input: Tensor) -> Tensor:
return self.mobile_net(input)


Expand All @@ -913,7 +913,7 @@ def __init__(self):
super().__init__()
self.squeezenet = torchvision.models.squeezenet1_0()

def forward(self, input: Tensor):
def forward(self, input: Tensor) -> Tensor:
return self.squeezenet(input)


Expand All @@ -929,7 +929,7 @@ def __init__(self):
norm_layer=partial(nn.InstanceNorm2d, track_running_stats=False, affine=True)
)

def forward(self, input: Tensor):
def forward(self, input: Tensor) -> Tensor:
return self.mobilenet(input) / 10.0


Expand Down
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