1 files changed, 37 insertions, 34 deletions

diff --git a/text_recognizer/networks/efficientnet/mbconv.py b/text_recognizer/networks/efficientnet/mbconv.py
index beb7d57..98e9353 100644
--- a/text_recognizer/networks/efficientnet/mbconv.py
+++ b/text_recognizer/networks/efficientnet/mbconv.py
@@ -1,7 +1,7 @@
 """Mobile inverted residual block."""
 from typing import Optional, Tuple, Union
 
-import attr
+from attrs import define, field
 import torch
 from torch import nn, Tensor
 import torch.nn.functional as F
@@ -14,13 +14,13 @@ def _convert_stride(stride: Union[Tuple[int, int], int]) -> Tuple[int, int]:
     return (stride,) * 2 if isinstance(stride, int) else stride
 
 
-@attr.s(eq=False)
+@define(eq=False)
 class BaseModule(nn.Module):
     """Base sub module class."""
 
-    bn_momentum: float = attr.ib()
-    bn_eps: float = attr.ib()
-    block: nn.Sequential = attr.ib(init=False)
+    bn_momentum: float = field()
+    bn_eps: float = field()
+    block: nn.Sequential = field(init=False)
 
     def __attrs_pre_init__(self) -> None:
         super().__init__()
@@ -36,12 +36,12 @@ class BaseModule(nn.Module):
         return self.block(x)
 
 
-@attr.s(auto_attribs=True, eq=False)
+@define(auto_attribs=True, eq=False)
 class InvertedBottleneck(BaseModule):
     """Inverted bottleneck module."""
 
-    in_channels: int = attr.ib()
-    out_channels: int = attr.ib()
+    in_channels: int = field()
+    out_channels: int = field()
 
     def _build(self) -> None:
         self.block = nn.Sequential(
@@ -60,13 +60,13 @@ class InvertedBottleneck(BaseModule):
         )
 
 
-@attr.s(auto_attribs=True, eq=False)
+@define(auto_attribs=True, eq=False)
 class Depthwise(BaseModule):
     """Depthwise convolution module."""
 
-    channels: int = attr.ib()
-    kernel_size: int = attr.ib()
-    stride: int = attr.ib()
+    channels: int = field()
+    kernel_size: int = field()
+    stride: int = field()
 
     def _build(self) -> None:
         self.block = nn.Sequential(
@@ -85,13 +85,13 @@ class Depthwise(BaseModule):
         )
 
 
-@attr.s(auto_attribs=True, eq=False)
+@define(auto_attribs=True, eq=False)
 class SqueezeAndExcite(BaseModule):
     """Sequeeze and excite module."""
 
-    in_channels: int = attr.ib()
-    channels: int = attr.ib()
-    se_ratio: float = attr.ib()
+    in_channels: int = field()
+    channels: int = field()
+    se_ratio: float = field()
 
     def _build(self) -> None:
         num_squeezed_channels = max(1, int(self.in_channels * self.se_ratio))
@@ -110,12 +110,12 @@ class SqueezeAndExcite(BaseModule):
         )
 
 
-@attr.s(auto_attribs=True, eq=False)
+@define(auto_attribs=True, eq=False)
 class Pointwise(BaseModule):
     """Pointwise module."""
 
-    in_channels: int = attr.ib()
-    out_channels: int = attr.ib()
+    in_channels: int = field()
+    out_channels: int = field()
 
     def _build(self) -> None:
         self.block = nn.Sequential(
@@ -133,32 +133,35 @@ class Pointwise(BaseModule):
         )
 
 
-@attr.s(eq=False)
+@define(eq=False)
 class MBConvBlock(nn.Module):
     """Mobile Inverted Residual Bottleneck block."""
 
     def __attrs_pre_init__(self) -> None:
         super().__init__()
 
-    in_channels: int = attr.ib()
-    out_channels: int = attr.ib()
-    kernel_size: Tuple[int, int] = attr.ib()
-    stride: Tuple[int, int] = attr.ib(converter=_convert_stride)
-    bn_momentum: float = attr.ib()
-    bn_eps: float = attr.ib()
-    se_ratio: float = attr.ib()
-    expand_ratio: int = attr.ib()
-    pad: Tuple[int, int, int, int] = attr.ib(init=False)
-    _inverted_bottleneck: Optional[InvertedBottleneck] = attr.ib(init=False)
-    _depthwise: nn.Sequential = attr.ib(init=False)
-    _squeeze_excite: nn.Sequential = attr.ib(init=False)
-    _pointwise: nn.Sequential = attr.ib(init=False)
+    in_channels: int = field()
+    out_channels: int = field()
+    kernel_size: Tuple[int, int] = field()
+    stride: Tuple[int, int] = field(converter=_convert_stride)
+    bn_momentum: float = field()
+    bn_eps: float = field()
+    se_ratio: float = field()
+    expand_ratio: int = field()
+    pad: Tuple[int, int, int, int] = field(init=False)
+    _inverted_bottleneck: Optional[InvertedBottleneck] = field(init=False)
+    _depthwise: nn.Sequential = field(init=False)
+    _squeeze_excite: nn.Sequential = field(init=False)
+    _pointwise: nn.Sequential = field(init=False)
 
     @pad.default
     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 - 1,
+                (self.kernel_size - 1) // 2,
+            ) * 2
         return ((self.kernel_size - 1) // 2,) * 4
 
     def __attrs_post_init__(self) -> None: