Lint files

1 files changed, 7 insertions, 10 deletions

diff --git a/text_recognizer/networks/conv_transformer.py b/text_recognizer/networks/conv_transformer.py
index 714792e..60c0ef8 100644
--- a/text_recognizer/networks/conv_transformer.py
+++ b/text_recognizer/networks/conv_transformer.py
@@ -1,6 +1,6 @@
 """Vision transformer for character recognition."""
 import math
-from typing import Tuple
+from typing import Tuple, Type
 
 from torch import nn, Tensor
 
@@ -24,6 +24,8 @@ class ConvTransformer(nn.Module):
         pad_index: Tensor,
         encoder: nn.Module,
         decoder: Decoder,
+        pixel_pos_embedding: Type[nn.Module],
+        token_pos_embedding: Type[nn.Module],
     ) -> None:
         super().__init__()
         self.input_dims = input_dims
@@ -43,11 +45,7 @@ class ConvTransformer(nn.Module):
                 out_channels=self.hidden_dim,
                 kernel_size=1,
             ),
-            PositionalEncoding2D(
-                hidden_dim=self.hidden_dim,
-                max_h=self.input_dims[1],
-                max_w=self.input_dims[2],
-            ),
+            pixel_pos_embedding,
             nn.Flatten(start_dim=2),
         )
 
@@ -57,9 +55,8 @@ class ConvTransformer(nn.Module):
         )
 
         # Positional encoding for decoder tokens.
-        self.token_pos_encoder = PositionalEncoding(
-            hidden_dim=self.hidden_dim, dropout_rate=self.dropout_rate
-        )
+        self.token_pos_embedding = token_pos_embedding
+
         # Head
         self.head = nn.Linear(
             in_features=self.hidden_dim, out_features=self.num_classes
@@ -119,7 +116,7 @@ class ConvTransformer(nn.Module):
         trg = trg.long()
         trg_mask = trg != self.pad_index
         trg = self.token_embedding(trg) * math.sqrt(self.hidden_dim)
-        trg = self.token_pos_encoder(trg)
+        trg = self.token_pos_embedding(trg)
         out = self.decoder(x=trg, context=src, mask=trg_mask)
         logits = self.head(out)  # [B, Sy, T]
         logits = logits.permute(0, 2, 1)  # [B, T, Sy]