1 files changed, 0 insertions, 107 deletions
diff --git a/text_recognizer/networks/transformer/layers.py b/text_recognizer/networks/transformer/layers.py
deleted file mode 100644
index 4263f52..0000000
--- a/text_recognizer/networks/transformer/layers.py
+++ /dev/null
@@ -1,107 +0,0 @@
-"""Transformer attention layer."""
-from copy import deepcopy
-from typing import Any, Optional, Tuple, Type
-
-from torch import nn, Tensor
-
-from text_recognizer.networks.transformer.attention import Attention
-from text_recognizer.networks.transformer.mlp import FeedForward
-from text_recognizer.networks.transformer.residual import Residual
-
-
-class AttentionLayers(nn.Module):
-    """Standard transfomer layer."""
-
-    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__()
-        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."""
-        if self.cross_attn is not None:
-            return "a", "c", "f"
-        return "a", "f"
-
-    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_attn)
-            elif layer_type == "c":
-                layer = deepcopy(cross_attn)
-            elif layer_type == "f":
-                layer = deepcopy(ff)
-            layers.append(nn.ModuleList([deepcopy(norm), layer, Residual()]))
-        return layers
-
-    def forward(
-        self,
-        x: Tensor,
-        context: Optional[Tensor] = None,
-        input_mask: Optional[Tensor] = None,
-        context_mask: Optional[Tensor] = None,
-    ) -> Tensor:
-        """Forward pass."""
-        for i, (layer_type, (norm, block, residual_fn)) in enumerate(
-            zip(self.layer_types, self.layers)
-        ):
-            is_last = i == len(self.layers) - 1
-            residual = x
-
-            if self.pre_norm:
-                x = norm(x)
-
-            if layer_type == "a":
-                out = block(x=x, input_mask=input_mask)
-            elif layer_type == "c":
-                out = block(
-                    x, context=context, input_mask=input_mask, context_mask=context_mask
-                )
-            elif layer_type == "f":
-                out = block(x)
-
-            x = residual_fn(out, residual)
-
-            if not self.pre_norm and not is_last:
-                x = norm(x)
-
-        return x
-
-
-class Decoder(AttentionLayers):
-    """Decoder module."""
-
-    def __init__(self, **kwargs: Any) -> None:
-        if "cross_attn" not in kwargs:
-            ValueError("Decoder requires cross attention.")
-
-        super().__init__(**kwargs)
-
-
-class Encoder(AttentionLayers):
-    """Encoder module."""
-
-    def __init__(self, **kwargs: Any) -> None:
-        if "cross_attn" in kwargs:
-            ValueError("Encoder requires cross attention.")
-
-        super().__init__(**kwargs)