Replace attr with attrs

1 files changed, 11 insertions, 13 deletions

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