attr bug fix, properly loading network

3 files changed, 11 insertions, 15 deletions

diff --git a/text_recognizer/networks/transformer/attention.py b/text_recognizer/networks/transformer/attention.py
index 2770dc1..9202cce 100644
--- a/text_recognizer/networks/transformer/attention.py
+++ b/text_recognizer/networks/transformer/attention.py
@@ -24,9 +24,9 @@ class Attention(nn.Module):
 
     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)
-    casual: bool = attr.ib(default=False)
     scale: float = attr.ib(init=False)
     dropout: nn.Dropout = attr.ib(init=False)
     fc: nn.Linear = attr.ib(init=False)
diff --git a/text_recognizer/networks/transformer/layers.py b/text_recognizer/networks/transformer/layers.py
index 9b2f236..66c9c50 100644
--- a/text_recognizer/networks/transformer/layers.py
+++ b/text_recognizer/networks/transformer/layers.py
@@ -30,8 +30,7 @@ class AttentionLayers(nn.Module):
     causal: bool = attr.ib(default=False)
     cross_attend: bool = attr.ib(default=False)
     pre_norm: bool = attr.ib(default=True)
-    rotary_emb: Optional[RotaryEmbedding] = attr.ib(default=None, init=False)
-    has_pos_emb: bool = attr.ib(init=False)
+    rotary_emb: Optional[RotaryEmbedding] = attr.ib(default=None)
     layer_types: Tuple[str, ...] = attr.ib(init=False)
     layers: nn.ModuleList = attr.ib(init=False)
     attn: partial = attr.ib(init=False)
@@ -40,12 +39,11 @@ class AttentionLayers(nn.Module):
 
     def __attrs_post_init__(self) -> None:
         """Post init configuration."""
-        self.has_pos_emb = True if self.rotary_emb is not None else False
         self.layer_types = self._get_layer_types() * self.depth
         attn = load_partial_fn(
             self.attn_fn, dim=self.dim, num_heads=self.num_heads, **self.attn_kwargs
         )
-        norm = load_partial_fn(self.norm_fn, dim=self.dim)
+        norm = load_partial_fn(self.norm_fn, normalized_shape=self.dim)
         ff = load_partial_fn(self.ff_fn, dim=self.dim, **self.ff_kwargs)
         self.layers = self._build_network(attn, norm, ff)
 
@@ -103,13 +101,11 @@ class AttentionLayers(nn.Module):
         return x
 
 
+@attr.s(auto_attribs=True)
 class Encoder(AttentionLayers):
-    def __init__(self, **kwargs: Any) -> None:
-        assert "causal" not in kwargs, "Cannot set causality on encoder"
-        super().__init__(causal=False, **kwargs)
+    causal: bool = attr.ib(default=False, init=False)
 
 
+@attr.s(auto_attribs=True)
 class Decoder(AttentionLayers):
-    def __init__(self, **kwargs: Any) -> None:
-        assert "causal" not in kwargs, "Cannot set causality on decoder"
-        super().__init__(causal=True, **kwargs)
+    causal: bool = attr.ib(default=True, init=False)
diff --git a/text_recognizer/networks/transformer/norm.py b/text_recognizer/networks/transformer/norm.py
index 8bc3221..4930adf 100644
--- a/text_recognizer/networks/transformer/norm.py
+++ b/text_recognizer/networks/transformer/norm.py
@@ -12,9 +12,9 @@ from torch import Tensor
 
 
 class ScaleNorm(nn.Module):
-    def __init__(self, dim: int, eps: float = 1.0e-5) -> None:
+    def __init__(self, normalized_shape: int, eps: float = 1.0e-5) -> None:
         super().__init__()
-        self.scale = dim ** -0.5
+        self.scale = normalized_shape ** -0.5
         self.eps = eps
         self.g = nn.Parameter(torch.ones(1))
 
@@ -24,9 +24,9 @@ class ScaleNorm(nn.Module):
 
 
 class PreNorm(nn.Module):
-    def __init__(self, dim: int, fn: Type[nn.Module]) -> None:
+    def __init__(self, normalized_shape: int, fn: Type[nn.Module]) -> None:
         super().__init__()
-        self.norm = nn.LayerNorm(dim)
+        self.norm = nn.LayerNorm(normalized_shape)
         self.fn = fn
 
     def forward(self, x: Tensor, **kwargs: Dict) -> Tensor: