Efficient net and non working transformer model.

1 files changed, 2 insertions, 10 deletions

diff --git a/text_recognizer/networks/vqvae/encoder.py b/text_recognizer/networks/vqvae/encoder.py
index b0cceed..65801df 100644
--- a/text_recognizer/networks/vqvae/encoder.py
+++ b/text_recognizer/networks/vqvae/encoder.py
@@ -11,10 +11,7 @@ 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 = [
@@ -138,12 +135,7 @@ 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)