Replace attr with attrs

3 files changed, 65 insertions, 62 deletions

diff --git a/text_recognizer/networks/efficientnet/efficientnet.py b/text_recognizer/networks/efficientnet/efficientnet.py
index 4c9ed75..cf64bcf 100644
--- a/text_recognizer/networks/efficientnet/efficientnet.py
+++ b/text_recognizer/networks/efficientnet/efficientnet.py
@@ -1,7 +1,7 @@
 """Efficientnet backbone."""
 from typing import Tuple
 
-import attr
+from attrs import define, field
 from torch import nn, Tensor
 
 from text_recognizer.networks.efficientnet.mbconv import MBConvBlock
@@ -12,7 +12,7 @@ from text_recognizer.networks.efficientnet.utils import (
 )
 
 
-@attr.s(eq=False)
+@define(eq=False)
 class EfficientNet(nn.Module):
     """Efficientnet without classification head."""
 
@@ -33,28 +33,28 @@ class EfficientNet(nn.Module):
         "l2": (4.3, 5.3, 0.5),
     }
 
-    arch: str = attr.ib()
-    params: Tuple[float, float, float] = attr.ib(default=None, init=False)
-    stochastic_dropout_rate: float = attr.ib(default=0.2)
-    bn_momentum: float = attr.ib(default=0.99)
-    bn_eps: float = attr.ib(default=1.0e-3)
-    depth: int = attr.ib(default=7)
-    out_channels: int = attr.ib(default=None, init=False)
-    _conv_stem: nn.Sequential = attr.ib(default=None, init=False)
-    _blocks: nn.ModuleList = attr.ib(default=None, init=False)
-    _conv_head: nn.Sequential = attr.ib(default=None, init=False)
+    arch: str = field()
+    params: Tuple[float, float, float] = field(default=None, init=False)
+    stochastic_dropout_rate: float = field(default=0.2)
+    bn_momentum: float = field(default=0.99)
+    bn_eps: float = field(default=1.0e-3)
+    depth: int = field(default=7)
+    out_channels: int = field(default=None, init=False)
+    _conv_stem: nn.Sequential = field(default=None, init=False)
+    _blocks: nn.ModuleList = field(default=None, init=False)
+    _conv_head: nn.Sequential = field(default=None, init=False)
 
     def __attrs_post_init__(self) -> None:
         """Post init configuration."""
         self._build()
 
     @depth.validator
-    def _check_depth(self, attribute: attr._make.Attribute, value: str) -> None:
+    def _check_depth(self, attribute, value: str) -> None:
         if not 5 <= value <= 7:
             raise ValueError(f"Depth has to be between 5 and 7, was: {value}")
 
     @arch.validator
-    def _check_arch(self, attribute: attr._make.Attribute, value: str) -> None:
+    def _check_arch(self, attribute, value: str) -> None:
         """Validates the efficientnet architecure."""
         if value not in self.archs:
             raise ValueError(f"{value} not a valid architecure.")
@@ -88,7 +88,9 @@ class EfficientNet(nn.Module):
             for _ in range(num_repeats):
                 self._blocks.append(
                     MBConvBlock(
-                        **args, bn_momentum=self.bn_momentum, bn_eps=self.bn_eps,
+                        **args,
+                        bn_momentum=self.bn_momentum,
+                        bn_eps=self.bn_eps,
                     )
                 )
                 args.in_channels = args.out_channels
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:
diff --git a/text_recognizer/networks/transformer/attention.py b/text_recognizer/networks/transformer/attention.py
index 87792a9..aa15b88 100644
--- a/text_recognizer/networks/transformer/attention.py
+++ b/text_recognizer/networks/transformer/attention.py
@@ -1,7 +1,7 @@
 """Implementes the attention module for the transformer."""
 from typing import Optional, Tuple
 
-import attr
+from attrs import define, field
 from einops import rearrange
 import torch
 from torch import einsum
@@ -15,22 +15,22 @@ from text_recognizer.networks.transformer.embeddings.rotary import (
 )
 
 
-@attr.s(eq=False)
+@define(eq=False)
 class Attention(nn.Module):
     """Standard attention."""
 
     def __attrs_pre_init__(self) -> None:
         super().__init__()
 
-    dim: int = attr.ib()
-    num_heads: int = attr.ib()
-    causal: bool = attr.ib(default=False)
-    dim_head: int = attr.ib(default=64)
-    dropout_rate: float = attr.ib(default=0.0)
-    rotary_embedding: Optional[RotaryEmbedding] = attr.ib(default=None)
-    scale: float = attr.ib(init=False)
-    dropout: nn.Dropout = attr.ib(init=False)
-    fc: nn.Linear = attr.ib(init=False)
+    dim: int = field()
+    num_heads: int = field()
+    causal: bool = field(default=False)
+    dim_head: int = field(default=64)
+    dropout_rate: float = field(default=0.0)
+    rotary_embedding: Optional[RotaryEmbedding] = field(default=None)
+    scale: float = field(init=False)
+    dropout: nn.Dropout = field(init=False)
+    fc: nn.Linear = field(init=False)
 
     def __attrs_post_init__(self) -> None:
         self.scale = self.dim ** -0.5
@@ -120,7 +120,6 @@ def apply_input_mask(
         input_mask = q_mask * k_mask
 
         energy = energy.masked_fill_(~input_mask, mask_value)
-        del input_mask
     return energy
 
 
@@ -133,5 +132,4 @@ def apply_causal_mask(
     mask = rearrange(r, "i -> () () i ()") < rearrange(r, "j -> () () () j")
     mask = F.pad(mask, (j - i, 0), value=False)
     energy.masked_fill_(mask, mask_value)
-    del mask
     return energy