Refactor padding

1 files changed, 1 insertions, 16 deletions

diff --git a/text_recognizer/networks/efficientnet/mbconv.py b/text_recognizer/networks/efficientnet/mbconv.py
index 64debd9..e090542 100644
--- a/text_recognizer/networks/efficientnet/mbconv.py
+++ b/text_recognizer/networks/efficientnet/mbconv.py
@@ -8,11 +8,6 @@ import torch.nn.functional as F
 from text_recognizer.networks.efficientnet.utils import stochastic_depth
 
 
-def _convert_stride(stride: Union[Tuple[int, int], int]) -> Tuple[int, int]:
-    """Converts int to tuple."""
-    return (stride,) * 2 if isinstance(stride, int) else stride
-
-
 class BaseModule(nn.Module):
     """Base sub module class."""
 
@@ -89,6 +84,7 @@ class Depthwise(BaseModule):
                 kernel_size=self.kernel_size,
                 stride=self.stride,
                 groups=self.channels,
+                padding="same",
                 bias=False,
             ),
             nn.BatchNorm2d(
@@ -187,22 +183,12 @@ class MBConvBlock(nn.Module):
         self.bn_eps = bn_eps
         self.se_ratio = se_ratio
         self.expand_ratio = expand_ratio
-        self.pad = self._configure_padding()
         self._inverted_bottleneck: Optional[InvertedBottleneck]
         self._depthwise: nn.Sequential
         self._squeeze_excite: nn.Sequential
         self._pointwise: nn.Sequential
         self._build()
 
-    def _configure_padding(self) -> Tuple[int, int, int, int]:
-        """Set padding for convolutional layers."""
-        if self.stride == (2, 2):
-            return (
-                (self.kernel_size - 1) // 2 - 1,
-                (self.kernel_size - 1) // 2,
-            ) * 2
-        return ((self.kernel_size - 1) // 2,) * 4
-
     def _build(self) -> None:
         has_se = self.se_ratio is not None and 0.0 < self.se_ratio < 1.0
         inner_channels = self.in_channels * self.expand_ratio
@@ -263,7 +249,6 @@ class MBConvBlock(nn.Module):
         if self._inverted_bottleneck is not None:
             x = self._inverted_bottleneck(x)
 
-        x = F.pad(x, self.pad)
         x = self._depthwise(x)
 
         if self._squeeze_excite is not None: