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PYRE_FIXME in classification_models.py #1636

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PYRE_FIXME in classification_models.py #1636

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42 changes: 10 additions & 32 deletions captum/testing/helpers/classification_models.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,23 +12,17 @@ class SigmoidModel(nn.Module):
-pytorch-and-make-your-life-simpler-ec5367895199
"""

# pyre-fixme[2]: Parameter must be annotated.
def __init__(self, num_in, num_hidden, num_out) -> None:
def __init__(self, num_in: int, num_hidden: int, num_out: int) -> None:
super().__init__()
# pyre-fixme[4]: Attribute must be annotated.
self.num_in = num_in
# pyre-fixme[4]: Attribute must be annotated.
self.num_hidden = num_hidden
# pyre-fixme[4]: Attribute must be annotated.
self.num_out = num_out
self.lin1 = nn.Linear(num_in, num_hidden)
self.lin2 = nn.Linear(num_hidden, num_out)
self.relu1 = nn.ReLU()
self.sigmoid = nn.Sigmoid()

# pyre-fixme[3]: Return type must be annotated.
# pyre-fixme[2]: Parameter must be annotated.
def forward(self, input):
def forward(self, input: torch.Tensor) -> torch.Tensor:
lin1 = self.lin1(input)
lin2 = self.lin2(self.relu1(lin1))
return self.sigmoid(lin2)
Expand All @@ -40,14 +34,12 @@ class SoftmaxModel(nn.Module):
https://adventuresinmachinelearning.com/pytorch-tutorial-deep-learning/
"""

# pyre-fixme[2]: Parameter must be annotated.
def __init__(self, num_in, num_hidden, num_out, inplace: bool = False) -> None:
def __init__(
self, num_in: int, num_hidden: int, num_out: int, inplace: bool = False
) -> None:
super().__init__()
# pyre-fixme[4]: Attribute must be annotated.
self.num_in = num_in
# pyre-fixme[4]: Attribute must be annotated.
self.num_hidden = num_hidden
# pyre-fixme[4]: Attribute must be annotated.
self.num_out = num_out
self.lin1 = nn.Linear(num_in, num_hidden)
self.lin2 = nn.Linear(num_hidden, num_hidden)
Expand All @@ -56,9 +48,7 @@ def __init__(self, num_in, num_hidden, num_out, inplace: bool = False) -> None:
self.relu2 = nn.ReLU(inplace=inplace)
self.softmax = nn.Softmax(dim=1)

# pyre-fixme[3]: Return type must be annotated.
# pyre-fixme[2]: Parameter must be annotated.
def forward(self, input):
def forward(self, input: torch.Tensor) -> torch.Tensor:
lin1 = self.relu1(self.lin1(input))
lin2 = self.relu2(self.lin2(lin1))
lin3 = self.lin3(lin2)
Expand All @@ -72,14 +62,10 @@ class SigmoidDeepLiftModel(nn.Module):
-pytorch-and-make-your-life-simpler-ec5367895199
"""

# pyre-fixme[2]: Parameter must be annotated.
def __init__(self, num_in, num_hidden, num_out) -> None:
def __init__(self, num_in: int, num_hidden: int, num_out: int) -> None:
super().__init__()
# pyre-fixme[4]: Attribute must be annotated.
self.num_in = num_in
# pyre-fixme[4]: Attribute must be annotated.
self.num_hidden = num_hidden
# pyre-fixme[4]: Attribute must be annotated.
self.num_out = num_out
self.lin1 = nn.Linear(num_in, num_hidden, bias=False)
self.lin2 = nn.Linear(num_hidden, num_out, bias=False)
Expand All @@ -88,9 +74,7 @@ def __init__(self, num_in, num_hidden, num_out) -> None:
self.relu1 = nn.ReLU()
self.sigmoid = nn.Sigmoid()

# pyre-fixme[3]: Return type must be annotated.
# pyre-fixme[2]: Parameter must be annotated.
def forward(self, input):
def forward(self, input: torch.Tensor) -> torch.Tensor:
lin1 = self.lin1(input)
lin2 = self.lin2(self.relu1(lin1))
return self.sigmoid(lin2)
Expand All @@ -102,14 +86,10 @@ class SoftmaxDeepLiftModel(nn.Module):
https://adventuresinmachinelearning.com/pytorch-tutorial-deep-learning/
"""

# pyre-fixme[2]: Parameter must be annotated.
def __init__(self, num_in, num_hidden, num_out) -> None:
def __init__(self, num_in: int, num_hidden: int, num_out: int) -> None:
super().__init__()
# pyre-fixme[4]: Attribute must be annotated.
self.num_in = num_in
# pyre-fixme[4]: Attribute must be annotated.
self.num_hidden = num_hidden
# pyre-fixme[4]: Attribute must be annotated.
self.num_out = num_out
self.lin1 = nn.Linear(num_in, num_hidden)
self.lin2 = nn.Linear(num_hidden, num_hidden)
Expand All @@ -121,9 +101,7 @@ def __init__(self, num_in, num_hidden, num_out) -> None:
self.relu2 = nn.ReLU()
self.softmax = nn.Softmax(dim=1)

# pyre-fixme[3]: Return type must be annotated.
# pyre-fixme[2]: Parameter must be annotated.
def forward(self, input):
def forward(self, input: torch.Tensor) -> torch.Tensor:
lin1 = self.relu1(self.lin1(input))
lin2 = self.relu2(self.lin2(lin1))
lin3 = self.lin3(lin2)
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