fix: refactor AttentionLayers

1 files changed, 25 insertions, 29 deletions

diff --git a/text_recognizer/networks/transformer/layers.py b/text_recognizer/networks/transformer/layers.py
index fc32f20..4263f52 100644
--- a/text_recognizer/networks/transformer/layers.py
+++ b/text_recognizer/networks/transformer/layers.py
@@ -2,7 +2,6 @@
 from copy import deepcopy
 from typing import Any, Optional, Tuple, Type
 
-import attr
 from torch import nn, Tensor
 
 from text_recognizer.networks.transformer.attention import Attention
@@ -10,26 +9,24 @@ from text_recognizer.networks.transformer.mlp import FeedForward
 from text_recognizer.networks.transformer.residual import Residual
 
 
-@attr.s(eq=False)
 class AttentionLayers(nn.Module):
     """Standard transfomer layer."""
 
-    def __attrs_pre_init__(self) -> None:
+    def __init__(
+        self,
+        depth: int,
+        self_attn: Attention,
+        norm: Type[nn.Module],
+        ff: FeedForward,
+        cross_attn: Optional[Attention] = None,
+        pre_norm: bool = True,
+        has_pos_emb: bool = True,
+    ) -> None:
         super().__init__()
-
-    depth: int = attr.ib()
-    self_attn: Attention = attr.ib()
-    norm: Type[nn.Module] = attr.ib()
-    ff: FeedForward = attr.ib()
-    cross_attn: Optional[Attention] = attr.ib(default=None)
-    pre_norm: bool = attr.ib(default=True)
-    has_pos_emb: bool = attr.ib(default=False)
-    layer_types: Tuple[str, ...] = attr.ib(init=False)
-    layers: nn.ModuleList = attr.ib(init=False)
-
-    def __attrs_post_init__(self) -> None:
-        self.layer_types = self._get_layer_types() * self.depth
-        self.layers = self._build()
+        self.pre_norm = pre_norm
+        self.has_pos_emb = has_pos_emb
+        self.layer_types = self._get_layer_types() * depth
+        self.layers = self._build(self_attn, norm, ff, cross_attn)
 
     def _get_layer_types(self) -> Tuple:
         """Get layer specification."""
@@ -37,24 +34,23 @@ class AttentionLayers(nn.Module):
             return "a", "c", "f"
         return "a", "f"
 
-    def _delete(self) -> None:
-        del self.self_attn
-        del self.ff
-        del self.norm
-        del self.cross_attn
-
-    def _build(self) -> nn.ModuleList:
+    def _build(
+        self,
+        self_attn: Attention,
+        norm: Type[nn.Module],
+        ff: FeedForward,
+        cross_attn: Optional[Attention],
+    ) -> nn.ModuleList:
         """Configures transformer network."""
         layers = nn.ModuleList([])
         for layer_type in self.layer_types:
             if layer_type == "a":
-                layer = deepcopy(self.self_attn)
+                layer = deepcopy(self_attn)
             elif layer_type == "c":
-                layer = deepcopy(self.cross_attn)
+                layer = deepcopy(cross_attn)
             elif layer_type == "f":
-                layer = deepcopy(self.ff)
-            layers.append(nn.ModuleList([deepcopy(self.norm), layer, Residual()]))
-        self._delete()
+                layer = deepcopy(ff)
+            layers.append(nn.ModuleList([deepcopy(norm), layer, Residual()]))
         return layers
 
     def forward(