2 files changed, 25 insertions, 5 deletions
diff --git a/text_recognizer/networks/vqvae/decoder.py b/text_recognizer/networks/vqvae/decoder.py
index 8847aba..67ed0d9 100644
--- a/text_recognizer/networks/vqvae/decoder.py
+++ b/text_recognizer/networks/vqvae/decoder.py
@@ -44,7 +44,12 @@ class Decoder(nn.Module):
 
         # Configure encoder.
         self.decoder = self._build_decoder(
-            channels, kernel_sizes, strides, num_residual_layers, activation, dropout,
+            channels,
+            kernel_sizes,
+            strides,
+            num_residual_layers,
+            activation,
+            dropout,
         )
 
     def _build_decompression_block(
@@ -73,7 +78,9 @@ class Decoder(nn.Module):
             )
 
             if i < len(self.upsampling):
-                modules.append(nn.Upsample(size=self.upsampling[i]),)
+                modules.append(
+                    nn.Upsample(size=self.upsampling[i]),
+                )
 
             if dropout is not None:
                 modules.append(dropout)
@@ -102,7 +109,12 @@ class Decoder(nn.Module):
     ) -> nn.Sequential:
 
         self.res_block.append(
-            nn.Conv2d(self.embedding_dim, channels[0], kernel_size=1, stride=1,)
+            nn.Conv2d(
+                self.embedding_dim,
+                channels[0],
+                kernel_size=1,
+                stride=1,
+            )
         )
 
         # Bottleneck module.
diff --git a/text_recognizer/networks/vqvae/encoder.py b/text_recognizer/networks/vqvae/encoder.py
index d3adac5..ede5c31 100644
--- a/text_recognizer/networks/vqvae/encoder.py
+++ b/text_recognizer/networks/vqvae/encoder.py
@@ -11,7 +11,10 @@ from text_recognizer.networks.vqvae.vector_quantizer import VectorQuantizer
 
 class _ResidualBlock(nn.Module):
     def __init__(
-        self, in_channels: int, out_channels: int, dropout: Optional[Type[nn.Module]],
+        self,
+        in_channels: int,
+        out_channels: int,
+        dropout: Optional[Type[nn.Module]],
     ) -> None:
         super().__init__()
         self.block = [
@@ -135,7 +138,12 @@ class Encoder(nn.Module):
         )
 
         encoder.append(
-            nn.Conv2d(channels[-1], self.embedding_dim, kernel_size=1, stride=1,)
+            nn.Conv2d(
+                channels[-1],
+                self.embedding_dim,
+                kernel_size=1,
+                stride=1,
+            )
         )
 
         return nn.Sequential(*encoder)