Fix non-existent variable & formatting

torchlayers/convolution.py

@@ -28,7 +28,9 @@ class _Conv(module.InferDimension):
     """
 
     def __init__(
-        self, dispatcher: typing.Dict[int, typing.Any], **kwargs,
+        self,
+        dispatcher: typing.Dict[int, typing.Any],
+        **kwargs,
     ):
         super().__init__(dispatcher=dispatcher, initializer=self._pad, **kwargs)
 
@@ -585,7 +587,7 @@ def __init__(self, module: torch.nn.Module, projection: torch.nn.Module = None):
     def forward(self, inputs):
         output = self.module(inputs)
         if self.projection is not None:
-            inputs = self.projections(inputs)
+            inputs = self.projection(inputs)
         return output + inputs
 
 
@@ -794,17 +796,21 @@ class SqueezeExcitation(torch.nn.Module):
     """
 
     def __init__(
-        self, in_channels: int, hidden: int = None, activation=None, sigmoid=None,
+        self,
+        in_channels: int,
+        hidden: int = None,
+        activation=None,
+        sigmoid=None,
     ):
         super().__init__()
         self.in_channels: int = in_channels
         self.hidden: int = hidden if hidden is not None else in_channels // 16
-        self.activation: typing.Callable[
-            [torch.Tensor], torch.Tensor
-        ] = activation if activation is not None else torch.nn.ReLU()
-        self.sigmoid: typing.Callable[
-            [torch.Tensor], torch.Tensor
-        ] = sigmoid if sigmoid is not None else torch.nn.Sigmoid()
+        self.activation: typing.Callable[[torch.Tensor], torch.Tensor] = (
+            activation if activation is not None else torch.nn.ReLU()
+        )
+        self.sigmoid: typing.Callable[[torch.Tensor], torch.Tensor] = (
+            sigmoid if sigmoid is not None else torch.nn.Sigmoid()
+        )
 
         self._pooling = pooling.GlobalAvgPool()
         self._first = torch.nn.Linear(in_channels, self.hidden)
@@ -847,7 +853,11 @@ class Fire(torch.nn.Module):
     """
 
     def __init__(
-        self, in_channels: int, out_channels: int, hidden_channels=None, p: float = 0.5,
+        self,
+        in_channels: int,
+        out_channels: int,
+        hidden_channels=None,
+        p: float = 0.5,
     ):
         super().__init__()
         self.in_channels = in_channels
@@ -951,10 +961,12 @@ def _add_batchnorm(block, channels):
 
         # Argument assignments
         self.in_channels: int = in_channels
-        self.hidden_channels: int = hidden_channels if hidden_channels is not None else in_channels * 4
-        self.activation: typing.Callable[
-            [torch.Tensor], torch.Tensor
-        ] = torch.nn.ReLU6() if activation is None else activation
+        self.hidden_channels: int = (
+            hidden_channels if hidden_channels is not None else in_channels * 4
+        )
+        self.activation: typing.Callable[[torch.Tensor], torch.Tensor] = (
+            torch.nn.ReLU6() if activation is None else activation
+        )
         self.batchnorm: bool = batchnorm
         self.squeeze_excitation: bool = squeeze_excitation
         self.squeeze_excitation_hidden: int = squeeze_excitation_hidden
@@ -1005,7 +1017,13 @@ def _add_batchnorm(block, channels):
         # Squeeze to in channels
         squeeze = torch.nn.Sequential(
             *_add_batchnorm(
-                [Conv(self.hidden_channels, self.in_channels, kernel_size=1,),],
+                [
+                    Conv(
+                        self.hidden_channels,
+                        self.in_channels,
+                        kernel_size=1,
+                    ),
+                ],
                 self.in_channels,
             )
         )