1 files changed, 14 insertions, 21 deletions
diff --git a/text_recognizer/networks/vqvae/residual.py b/text_recognizer/networks/vqvae/residual.py
index 46b091d..bdff9eb 100644
--- a/text_recognizer/networks/vqvae/residual.py
+++ b/text_recognizer/networks/vqvae/residual.py
@@ -3,59 +3,52 @@ import attr
 from torch import nn
 from torch import Tensor
 
+from text_recognizer.networks.util import activation_function
 from text_recognizer.networks.vqvae.norm import Normalize
 
 
 @attr.s(eq=False)
 class Residual(nn.Module):
     in_channels: int = attr.ib()
-    out_channels: int = attr.ib()
-    dropout_rate: float = attr.ib(default=0.0)
+    residual_channels: int = attr.ib()
     use_norm: bool = attr.ib(default=False)
+    activation: str = attr.ib(default="relu")
 
     def __attrs_post_init__(self) -> None:
         """Post init configuration."""
         super().__init__()
         self.block = self._build_res_block()
-        if self.in_channels != self.out_channels:
-            self.conv_shortcut = nn.Conv2d(
-                in_channels=self.in_channels,
-                out_channels=self.out_channels,
-                kernel_size=3,
-                stride=1,
-                padding=1,
-            )
-        else:
-            self.conv_shortcut = None
 
     def _build_res_block(self) -> nn.Sequential:
         """Build residual block."""
         block = []
+        activation_fn = activation_function(activation=self.activation)
+
         if self.use_norm:
             block.append(Normalize(num_channels=self.in_channels))
+
         block += [
-            nn.Mish(),
+            activation_fn,
             nn.Conv2d(
                 self.in_channels,
-                self.out_channels,
+                self.residual_channels,
                 kernel_size=3,
                 padding=1,
                 bias=False,
             ),
         ]
-        if self.dropout_rate:
-            block += [nn.Dropout(p=self.dropout_rate)]
 
         if self.use_norm:
-            block.append(Normalize(num_channels=self.out_channels))
+            block.append(Normalize(num_channels=self.residual_channels))
 
         block += [
-            nn.Mish(),
-            nn.Conv2d(self.out_channels, self.out_channels, kernel_size=1, bias=False),
+            activation_fn,
+            nn.Conv2d(
+                self.residual_channels, self.in_channels, kernel_size=1, bias=False
+            ),
         ]
         return nn.Sequential(*block)
 
     def forward(self, x: Tensor) -> Tensor:
         """Apply the residual forward pass."""
-        residual = self.conv_shortcut(x) if self.conv_shortcut is not None else x
-        return residual + self.block(x)
+        return x + self.block(x)