Replace attr with attrs

15 files changed, 144 insertions, 142 deletions

diff --git a/text_recognizer/data/base_data_module.py b/text_recognizer/data/base_data_module.py
index 15c286a..a0c8416 100644
--- a/text_recognizer/data/base_data_module.py
+++ b/text_recognizer/data/base_data_module.py
@@ -2,7 +2,7 @@
 from pathlib import Path
 from typing import Callable, Dict, Optional, Tuple, Type, TypeVar
 
-import attr
+from attrs import define, field
 from pytorch_lightning import LightningDataModule
 from torch.utils.data import DataLoader
 
@@ -20,29 +20,29 @@ def load_and_print_info(data_module_class: type) -> None:
     print(dataset)
 
 
-@attr.s(repr=False)
+@define(repr=False)
 class BaseDataModule(LightningDataModule):
     """Base PyTorch Lightning DataModule."""
 
-    def __attrs_pre_init__(self) -> None:
+    def __attrs_post_init__(self) -> None:
         """Pre init constructor."""
         super().__init__()
 
-    mapping: Type[AbstractMapping] = attr.ib()
-    transform: Optional[Callable] = attr.ib(default=None)
-    test_transform: Optional[Callable] = attr.ib(default=None)
-    target_transform: Optional[Callable] = attr.ib(default=None)
-    train_fraction: float = attr.ib(default=0.8)
-    batch_size: int = attr.ib(default=16)
-    num_workers: int = attr.ib(default=0)
-    pin_memory: bool = attr.ib(default=True)
+    mapping: Type[AbstractMapping] = field()
+    transform: Optional[Callable] = field(default=None)
+    test_transform: Optional[Callable] = field(default=None)
+    target_transform: Optional[Callable] = field(default=None)
+    train_fraction: float = field(default=0.8)
+    batch_size: int = field(default=16)
+    num_workers: int = field(default=0)
+    pin_memory: bool = field(default=True)
 
     # Placeholders
-    data_train: BaseDataset = attr.ib(init=False, default=None)
-    data_val: BaseDataset = attr.ib(init=False, default=None)
-    data_test: BaseDataset = attr.ib(init=False, default=None)
-    dims: Tuple[int, ...] = attr.ib(init=False, default=None)
-    output_dims: Tuple[int, ...] = attr.ib(init=False, default=None)
+    data_train: BaseDataset = field(init=False, default=None)
+    data_val: BaseDataset = field(init=False, default=None)
+    data_test: BaseDataset = field(init=False, default=None)
+    dims: Tuple[int, ...] = field(init=False, default=None)
+    output_dims: Tuple[int, ...] = field(init=False, default=None)
 
     @classmethod
     def data_dirname(cls: T) -> Path:
diff --git a/text_recognizer/data/base_dataset.py b/text_recognizer/data/base_dataset.py
index b9567c7..c57cbcc 100644
--- a/text_recognizer/data/base_dataset.py
+++ b/text_recognizer/data/base_dataset.py
@@ -1,7 +1,7 @@
 """Base PyTorch Dataset class."""
 from typing import Callable, Dict, Optional, Sequence, Tuple, Union
 
-import attr
+from attrs import define, field
 import torch
 from torch import Tensor
 from torch.utils.data import Dataset
@@ -9,7 +9,7 @@ from torch.utils.data import Dataset
 from text_recognizer.data.transforms.load_transform import load_transform_from_file
 
 
-@attr.s
+@define
 class BaseDataset(Dataset):
     r"""Base Dataset class that processes data and targets through optional transfroms.
 
@@ -21,10 +21,10 @@ class BaseDataset(Dataset):
             target transforms.
     """
 
-    data: Union[Sequence, Tensor] = attr.ib()
-    targets: Union[Sequence, Tensor] = attr.ib()
-    transform: Union[Optional[Callable], str] = attr.ib(default=None)
-    target_transform: Union[Optional[Callable], str] = attr.ib(default=None)
+    data: Union[Sequence, Tensor] = field()
+    targets: Union[Sequence, Tensor] = field()
+    transform: Union[Optional[Callable], str] = field(default=None)
+    target_transform: Union[Optional[Callable], str] = field(default=None)
 
     def __attrs_pre_init__(self) -> None:
         """Pre init constructor."""
diff --git a/text_recognizer/data/emnist.py b/text_recognizer/data/emnist.py
index dc8d31a..94882bf 100644
--- a/text_recognizer/data/emnist.py
+++ b/text_recognizer/data/emnist.py
@@ -6,7 +6,7 @@ import shutil
 from typing import Dict, List, Optional, Sequence, Set, Tuple
 import zipfile
 
-import attr
+from attrs import define
 import h5py
 from loguru import logger as log
 import numpy as np
@@ -35,7 +35,7 @@ ESSENTIALS_FILENAME = (
 )
 
 
-@attr.s(auto_attribs=True)
+@define(auto_attribs=True)
 class EMNIST(BaseDataModule):
     """Lightning DataModule class for loading EMNIST dataset.
 
diff --git a/text_recognizer/data/emnist_lines.py b/text_recognizer/data/emnist_lines.py
index c267286..43d55b9 100644
--- a/text_recognizer/data/emnist_lines.py
+++ b/text_recognizer/data/emnist_lines.py
@@ -3,7 +3,7 @@ from collections import defaultdict
 from pathlib import Path
 from typing import DefaultDict, List, Tuple
 
-import attr
+from attrs import define, field
 import h5py
 from loguru import logger as log
 import numpy as np
@@ -33,17 +33,17 @@ IMAGE_X_PADDING = 28
 MAX_OUTPUT_LENGTH = 89  # Same as IAMLines
 
 
-@attr.s(auto_attribs=True, repr=False)
+@define(auto_attribs=True, repr=False)
 class EMNISTLines(BaseDataModule):
     """EMNIST Lines dataset: synthetic handwritten lines dataset made from EMNIST."""
 
-    max_length: int = attr.ib(default=128)
-    min_overlap: float = attr.ib(default=0.0)
-    max_overlap: float = attr.ib(default=0.33)
-    num_train: int = attr.ib(default=10_000)
-    num_val: int = attr.ib(default=2_000)
-    num_test: int = attr.ib(default=2_000)
-    emnist: EMNIST = attr.ib(init=False, default=None)
+    max_length: int = field(default=128)
+    min_overlap: float = field(default=0.0)
+    max_overlap: float = field(default=0.33)
+    num_train: int = field(default=10_000)
+    num_val: int = field(default=2_000)
+    num_test: int = field(default=2_000)
+    emnist: EMNIST = field(init=False, default=None)
 
     def __attrs_post_init__(self) -> None:
         """Post init constructor."""
diff --git a/text_recognizer/data/iam.py b/text_recognizer/data/iam.py
index 766f3e0..8166863 100644
--- a/text_recognizer/data/iam.py
+++ b/text_recognizer/data/iam.py
@@ -9,7 +9,7 @@ from typing import Any, Dict, List
 import xml.etree.ElementTree as ElementTree
 import zipfile
 
-import attr
+from attrs import define, field
 from boltons.cacheutils import cachedproperty
 from loguru import logger as log
 import toml
@@ -27,7 +27,7 @@ DOWNSAMPLE_FACTOR = 2  # If images were downsampled, the regions must also be.
 LINE_REGION_PADDING = 16  # Add this many pixels around the exact coordinates.
 
 
-@attr.s(auto_attribs=True)
+@define(auto_attribs=True)
 class IAM(BaseDataModule):
     r"""The IAM Lines dataset.
 
@@ -44,7 +44,7 @@ class IAM(BaseDataModule):
         contributed to one set only.
     """
 
-    metadata: Dict = attr.ib(init=False, default=toml.load(METADATA_FILENAME))
+    metadata: Dict = field(init=False, default=toml.load(METADATA_FILENAME))
 
     def prepare_data(self) -> None:
         """Prepares the IAM dataset."""
diff --git a/text_recognizer/data/iam_extended_paragraphs.py b/text_recognizer/data/iam_extended_paragraphs.py
index 22d00f1..52c10c3 100644
--- a/text_recognizer/data/iam_extended_paragraphs.py
+++ b/text_recognizer/data/iam_extended_paragraphs.py
@@ -1,5 +1,5 @@
 """IAM original and sythetic dataset class."""
-import attr
+from attrs import define, field
 from torch.utils.data import ConcatDataset
 
 from text_recognizer.data.base_data_module import BaseDataModule, load_and_print_info
@@ -8,7 +8,7 @@ from text_recognizer.data.iam_synthetic_paragraphs import IAMSyntheticParagraphs
 from text_recognizer.data.transforms.load_transform import load_transform_from_file
 
 
-@attr.s(auto_attribs=True, repr=False)
+@define(auto_attribs=True, repr=False)
 class IAMExtendedParagraphs(BaseDataModule):
     """A dataset with synthetic and real handwritten paragraph."""
 
diff --git a/text_recognizer/data/iam_lines.py b/text_recognizer/data/iam_lines.py
index a79c202..34cf605 100644
--- a/text_recognizer/data/iam_lines.py
+++ b/text_recognizer/data/iam_lines.py
@@ -7,7 +7,7 @@ import json
 from pathlib import Path
 from typing import List, Sequence, Tuple
 
-import attr
+from attrs import define, field
 from loguru import logger as log
 import numpy as np
 from PIL import Image, ImageFile, ImageOps
@@ -35,14 +35,14 @@ MAX_LABEL_LENGTH = 89
 MAX_WORD_PIECE_LENGTH = 72
 
 
-@attr.s(auto_attribs=True, repr=False)
+@define(auto_attribs=True, repr=False)
 class IAMLines(BaseDataModule):
     """IAM handwritten lines dataset."""
 
-    dims: Tuple[int, int, int] = attr.ib(
+    dims: Tuple[int, int, int] = field(
         init=False, default=(1, IMAGE_HEIGHT, IMAGE_WIDTH)
     )
-    output_dims: Tuple[int, int] = attr.ib(init=False, default=(MAX_LABEL_LENGTH, 1))
+    output_dims: Tuple[int, int] = field(init=False, default=(MAX_LABEL_LENGTH, 1))
 
     def prepare_data(self) -> None:
         """Creates the IAM lines dataset if not existing."""
diff --git a/text_recognizer/data/iam_paragraphs.py b/text_recognizer/data/iam_paragraphs.py
index 033b93e..b605bbc 100644
--- a/text_recognizer/data/iam_paragraphs.py
+++ b/text_recognizer/data/iam_paragraphs.py
@@ -3,7 +3,7 @@ import json
 from pathlib import Path
 from typing import Dict, List, Optional, Sequence, Tuple
 
-import attr
+from attrs import define, field
 from loguru import logger as log
 import numpy as np
 from PIL import Image, ImageOps
@@ -33,15 +33,15 @@ MAX_LABEL_LENGTH = 682
 MAX_WORD_PIECE_LENGTH = 451
 
 
-@attr.s(auto_attribs=True, repr=False)
+@define(auto_attribs=True, repr=False)
 class IAMParagraphs(BaseDataModule):
     """IAM handwriting database paragraphs."""
 
     # Placeholders
-    dims: Tuple[int, int, int] = attr.ib(
+    dims: Tuple[int, int, int] = field(
         init=False, default=(1, IMAGE_HEIGHT, IMAGE_WIDTH)
     )
-    output_dims: Tuple[int, int] = attr.ib(init=False, default=(MAX_LABEL_LENGTH, 1))
+    output_dims: Tuple[int, int] = field(init=False, default=(MAX_LABEL_LENGTH, 1))
 
     def prepare_data(self) -> None:
         """Create data for training/testing."""
@@ -86,7 +86,10 @@ class IAMParagraphs(BaseDataModule):
                 length=self.output_dims[0],
             )
             return BaseDataset(
-                data, targets, transform=transform, target_transform=target_transform,
+                data,
+                targets,
+                transform=transform,
+                target_transform=target_transform,
             )
 
         log.info(f"Loading IAM paragraph regions and lines for {stage}...")
diff --git a/text_recognizer/data/iam_synthetic_paragraphs.py b/text_recognizer/data/iam_synthetic_paragraphs.py
index ea59098..7143951 100644
--- a/text_recognizer/data/iam_synthetic_paragraphs.py
+++ b/text_recognizer/data/iam_synthetic_paragraphs.py
@@ -2,7 +2,7 @@
 import random
 from typing import Any, List, Sequence, Tuple
 
-import attr
+from attrs import define
 from loguru import logger as log
 import numpy as np
 from PIL import Image
@@ -34,7 +34,7 @@ PROCESSED_DATA_DIRNAME = (
 )
 
 
-@attr.s(auto_attribs=True, repr=False)
+@define(auto_attribs=True, repr=False)
 class IAMSyntheticParagraphs(IAMParagraphs):
     """IAM Handwriting database of synthetic paragraphs."""
 
diff --git a/text_recognizer/models/base.py b/text_recognizer/models/base.py
index 821cb69..bf3bc08 100644
--- a/text_recognizer/models/base.py
+++ b/text_recognizer/models/base.py
@@ -1,7 +1,7 @@
 """Base PyTorch Lightning model."""
 from typing import Any, Dict, List, Optional, Tuple, Type
 
-import attr
+from attrs import define, field
 import hydra
 from loguru import logger as log
 from omegaconf import DictConfig
@@ -14,7 +14,7 @@ import torchmetrics
 from text_recognizer.data.mappings.base import AbstractMapping
 
 
-@attr.s(eq=False)
+@define(eq=False)
 class BaseLitModel(LightningModule):
     """Abstract PyTorch Lightning class."""
 
@@ -22,22 +22,18 @@ class BaseLitModel(LightningModule):
         """Pre init constructor."""
         super().__init__()
 
-    network: Type[nn.Module] = attr.ib()
-    loss_fn: Type[nn.Module] = attr.ib()
-    optimizer_configs: DictConfig = attr.ib()
-    lr_scheduler_configs: Optional[DictConfig] = attr.ib()
-    mapping: Type[AbstractMapping] = attr.ib()
+    network: Type[nn.Module] = field()
+    loss_fn: Type[nn.Module] = field()
+    optimizer_configs: DictConfig = field()
+    lr_scheduler_configs: Optional[DictConfig] = field()
+    mapping: Type[AbstractMapping] = field()
 
     # Placeholders
-    train_acc: torchmetrics.Accuracy = attr.ib(
-        init=False, default=torchmetrics.Accuracy()
-    )
-    val_acc: torchmetrics.Accuracy = attr.ib(
-        init=False, default=torchmetrics.Accuracy()
-    )
-    test_acc: torchmetrics.Accuracy = attr.ib(
+    train_acc: torchmetrics.Accuracy = field(
         init=False, default=torchmetrics.Accuracy()
     )
+    val_acc: torchmetrics.Accuracy = field(init=False, default=torchmetrics.Accuracy())
+    test_acc: torchmetrics.Accuracy = field(init=False, default=torchmetrics.Accuracy())
 
     def optimizer_zero_grad(
         self,
diff --git a/text_recognizer/models/metrics.py b/text_recognizer/models/metrics.py
index f83c9e4..e59a830 100644
--- a/text_recognizer/models/metrics.py
+++ b/text_recognizer/models/metrics.py
@@ -1,20 +1,20 @@
 """Character Error Rate (CER)."""
 from typing import Set
 
-import attr
+from attrs import define, field
 import editdistance
 import torch
 from torch import Tensor
 from torchmetrics import Metric
 
 
-@attr.s(eq=False)
+@define(eq=False)
 class CharacterErrorRate(Metric):
     """Character error rate metric, computed using Levenshtein distance."""
 
-    ignore_indices: Set[Tensor] = attr.ib(converter=set)
-    error: Tensor = attr.ib(init=False)
-    total: Tensor = attr.ib(init=False)
+    ignore_indices: Set[Tensor] = field(converter=set)
+    error: Tensor = field(init=False)
+    total: Tensor = field(init=False)
 
     def __attrs_post_init__(self) -> None:
         super().__init__()
diff --git a/text_recognizer/models/transformer.py b/text_recognizer/models/transformer.py
index 7272f46..c5120fe 100644
--- a/text_recognizer/models/transformer.py
+++ b/text_recognizer/models/transformer.py
@@ -1,7 +1,7 @@
 """PyTorch Lightning model for base Transformers."""
 from typing import Set, Tuple
 
-import attr
+from attrs import define, field
 import torch
 from torch import Tensor
 
@@ -9,22 +9,22 @@ from text_recognizer.models.base import BaseLitModel
 from text_recognizer.models.metrics import CharacterErrorRate
 
 
-@attr.s(auto_attribs=True, eq=False)
+@define(auto_attribs=True, eq=False)
 class TransformerLitModel(BaseLitModel):
     """A PyTorch Lightning model for transformer networks."""
 
-    max_output_len: int = attr.ib(default=451)
-    start_token: str = attr.ib(default="<s>")
-    end_token: str = attr.ib(default="<e>")
-    pad_token: str = attr.ib(default="<p>")
+    max_output_len: int = field(default=451)
+    start_token: str = field(default="<s>")
+    end_token: str = field(default="<e>")
+    pad_token: str = field(default="<p>")
 
-    start_index: int = attr.ib(init=False)
-    end_index: int = attr.ib(init=False)
-    pad_index: int = attr.ib(init=False)
+    start_index: int = field(init=False)
+    end_index: int = field(init=False)
+    pad_index: int = field(init=False)
 
-    ignore_indices: Set[Tensor] = attr.ib(init=False)
-    val_cer: CharacterErrorRate = attr.ib(init=False)
-    test_cer: CharacterErrorRate = attr.ib(init=False)
+    ignore_indices: Set[Tensor] = field(init=False)
+    val_cer: CharacterErrorRate = field(init=False)
+    test_cer: CharacterErrorRate = field(init=False)
 
     def __attrs_post_init__(self) -> None:
         """Post init configuration."""
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