Remove attrs

1 files changed, 78 insertions, 43 deletions

diff --git a/text_recognizer/networks/efficientnet/mbconv.py b/text_recognizer/networks/efficientnet/mbconv.py
index 98e9353..64debd9 100644
--- a/text_recognizer/networks/efficientnet/mbconv.py
+++ b/text_recognizer/networks/efficientnet/mbconv.py
@@ -1,7 +1,6 @@
 """Mobile inverted residual block."""
 from typing import Optional, Tuple, Union
 
-from attrs import define, field
 import torch
 from torch import nn, Tensor
 import torch.nn.functional as F
@@ -14,18 +13,15 @@ def _convert_stride(stride: Union[Tuple[int, int], int]) -> Tuple[int, int]:
     return (stride,) * 2 if isinstance(stride, int) else stride
 
 
-@define(eq=False)
 class BaseModule(nn.Module):
     """Base sub module class."""
 
-    bn_momentum: float = field()
-    bn_eps: float = field()
-    block: nn.Sequential = field(init=False)
-
-    def __attrs_pre_init__(self) -> None:
+    def __init__(self, bn_momentum: float, bn_eps: float, block: nn.Sequential) -> None:
         super().__init__()
 
-    def __attrs_post_init__(self) -> None:
+        self.bn_momentum = bn_momentum
+        self.bn_eps = bn_eps
+        self.block = block
         self._build()
 
     def _build(self) -> None:
@@ -36,12 +32,20 @@ class BaseModule(nn.Module):
         return self.block(x)
 
 
-@define(auto_attribs=True, eq=False)
 class InvertedBottleneck(BaseModule):
     """Inverted bottleneck module."""
 
-    in_channels: int = field()
-    out_channels: int = field()
+    def __init__(
+        self,
+        bn_momentum: float,
+        bn_eps: float,
+        block: nn.Sequential,
+        in_channels: int,
+        out_channels: int,
+    ) -> None:
+        super().__init__(bn_momentum, bn_eps, block)
+        self.in_channels = in_channels
+        self.out_channels = out_channels
 
     def _build(self) -> None:
         self.block = nn.Sequential(
@@ -60,13 +64,22 @@ class InvertedBottleneck(BaseModule):
         )
 
 
-@define(auto_attribs=True, eq=False)
 class Depthwise(BaseModule):
     """Depthwise convolution module."""
 
-    channels: int = field()
-    kernel_size: int = field()
-    stride: int = field()
+    def __init__(
+        self,
+        bn_momentum: float,
+        bn_eps: float,
+        block: nn.Sequential,
+        channels: int,
+        kernel_size: int,
+        stride: int,
+    ) -> None:
+        super().__init__(bn_momentum, bn_eps, block)
+        self.channels = channels
+        self.kernel_size = kernel_size
+        self.stride = stride
 
     def _build(self) -> None:
         self.block = nn.Sequential(
@@ -85,13 +98,23 @@ class Depthwise(BaseModule):
         )
 
 
-@define(auto_attribs=True, eq=False)
 class SqueezeAndExcite(BaseModule):
     """Sequeeze and excite module."""
 
-    in_channels: int = field()
-    channels: int = field()
-    se_ratio: float = field()
+    def __init__(
+        self,
+        bn_momentum: float,
+        bn_eps: float,
+        block: nn.Sequential,
+        in_channels: int,
+        channels: int,
+        se_ratio: float,
+    ) -> None:
+        super().__init__(bn_momentum, bn_eps, block)
+
+        self.in_channels = in_channels
+        self.channels = channels
+        self.se_ratio = se_ratio
 
     def _build(self) -> None:
         num_squeezed_channels = max(1, int(self.in_channels * self.se_ratio))
@@ -110,12 +133,20 @@ class SqueezeAndExcite(BaseModule):
         )
 
 
-@define(auto_attribs=True, eq=False)
 class Pointwise(BaseModule):
     """Pointwise module."""
 
-    in_channels: int = field()
-    out_channels: int = field()
+    def __init__(
+        self,
+        bn_momentum: float,
+        bn_eps: float,
+        block: nn.Sequential,
+        in_channels: int,
+        out_channels: int,
+    ) -> None:
+        super().__init__(bn_momentum, bn_eps, block)
+        self.in_channels = in_channels
+        self.out_channels = out_channels
 
     def _build(self) -> None:
         self.block = nn.Sequential(
@@ -133,28 +164,36 @@ class Pointwise(BaseModule):
         )
 
 
-@define(eq=False)
 class MBConvBlock(nn.Module):
     """Mobile Inverted Residual Bottleneck block."""
 
-    def __attrs_pre_init__(self) -> None:
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: Tuple[int, int],
+        stride: Tuple[int, int],
+        bn_momentum: float,
+        bn_eps: float,
+        se_ratio: float,
+        expand_ratio: int,
+    ) -> None:
         super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.kernel_size = kernel_size
+        self.stride = stride
+        self.bn_momentum = bn_momentum
+        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()
 
-    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):
@@ -164,10 +203,6 @@ class MBConvBlock(nn.Module):
             ) * 2
         return ((self.kernel_size - 1) // 2,) * 4
 
-    def __attrs_post_init__(self) -> None:
-        """Post init configuration."""
-        self._build()
-
     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