3 files changed, 74 insertions, 10 deletions
diff --git a/text_recognizer/networks/transformer/mlp.py b/text_recognizer/networks/transformer/mlp.py
new file mode 100644
index 0000000..4028ab3
--- /dev/null
+++ b/text_recognizer/networks/transformer/mlp.py
@@ -0,0 +1,46 @@
+"""Feedforward layer in transformer.
+
+Stolen from lucidrains:
+    https://github.com/lucidrains/x-transformers/blob/main/x_transformers/x_transformers.py
+"""
+from typing import Optional
+
+from torch import nn
+from torch import Tensor
+import torch.nn.functional as F
+
+
+class GEGLU(nn.Module):
+    def __init__(self, dim_in: int, dim_out: int) -> None:
+        super().__init__()
+        self.fc = nn.Linear(dim_in, dim_out * 2)
+
+    def forward(self, x: Tensor) -> Tensor:
+        x, gate = self.fc(x).chunk(2, dim=-1)
+        return x * F.gelu(gate)
+
+
+class FeedForward(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        dim_out: Optional[int] = None,
+        expansion_factor: int = 4,
+        glu: bool = True,
+        dropout_rate: float = 0.0,
+    ) -> None:
+        super().__init__()
+        inner_dim = dim * expansion_factor
+        dim_out = dim_out if dim_out is not None else dim
+        in_projection = (
+            nn.Sequential(nn.Linear(dim, inner_dim), nn.GELU())
+            if not glu
+            else GEGLU(dim, inner_dim)
+        )
+
+        self.mlp = nn.Sequential(
+            in_projection, nn.Dropout(dropout_rate), nn.Linear(inner_dim, dim_out)
+        )
+
+    def forward(self, x: Tensor) -> Tensor:
+        return self.mlp(x)
diff --git a/text_recognizer/networks/transformer/norm.py b/text_recognizer/networks/transformer/norm.py
new file mode 100644
index 0000000..99a5291
--- /dev/null
+++ b/text_recognizer/networks/transformer/norm.py
@@ -0,0 +1,22 @@
+"""Normalization layers for transfromers.
+
+Copied from lucidrains:
+    https://github.com/lucidrains/x-transformers/blob/main/x_transformers/x_transformers.py
+
+"""
+from typing import Callable, Dict
+
+import torch
+from torch import nn
+from torch import Tensor
+
+
+class Rezero(nn.Module):
+    def __init__(self, fn: Callable) -> None:
+        super().__init__()
+        self.fn = fn
+        self.g = nn.Parameter(torch.zeros(1))
+
+    def forward(self, x: Tensor, **kwargs: Dict) -> Tensor:
+        x, *rest = self.fn(x, **kwargs)
+        return (x * self.g, *rest)
diff --git a/text_recognizer/networks/transformer/transformer.py b/text_recognizer/networks/transformer/transformer.py
index dd180c4..5ac2787 100644
--- a/text_recognizer/networks/transformer/transformer.py
+++ b/text_recognizer/networks/transformer/transformer.py
@@ -41,7 +41,7 @@ class _IntraLayerConnection(nn.Module):
         return self.norm(self.dropout(src) + residual)
 
 
-class _ConvolutionalLayer(nn.Module):
+class FeedForward(nn.Module):
     def __init__(
         self,
         hidden_dim: int,
@@ -82,9 +82,7 @@ class EncoderLayer(nn.Module):
     ) -> None:
         super().__init__()
         self.self_attention = MultiHeadAttention(hidden_dim, num_heads, dropout_rate)
-        self.cnn = _ConvolutionalLayer(
-            hidden_dim, expansion_dim, dropout_rate, activation
-        )
+        self.mlp = FeedForward(hidden_dim, expansion_dim, dropout_rate, activation)
         self.block1 = _IntraLayerConnection(dropout_rate, hidden_dim)
         self.block2 = _IntraLayerConnection(dropout_rate, hidden_dim)
 
@@ -99,10 +97,10 @@ class EncoderLayer(nn.Module):
 
         # Second block.
         # Apply 1D-convolution.
-        cnn_out = self.cnn(out)
+        mlp_out = self.mlp(out)
 
         # Add & norm.
-        out = self.block2(cnn_out, out)
+        out = self.block2(mlp_out, out)
 
         return out
 
@@ -148,9 +146,7 @@ class DecoderLayer(nn.Module):
         self.multihead_attention = MultiHeadAttention(
             hidden_dim, num_heads, dropout_rate
         )
-        self.cnn = _ConvolutionalLayer(
-            hidden_dim, expansion_dim, dropout_rate, activation
-        )
+        self.mlp = FeedForward(hidden_dim, expansion_dim, dropout_rate, activation)
         self.block1 = _IntraLayerConnection(dropout_rate, hidden_dim)
         self.block2 = _IntraLayerConnection(dropout_rate, hidden_dim)
         self.block3 = _IntraLayerConnection(dropout_rate, hidden_dim)
@@ -169,7 +165,7 @@ class DecoderLayer(nn.Module):
         out, _ = self.multihead_attention(trg, memory, memory, memory_mask)
         trg = self.block2(out, trg)
 
-        out = self.cnn(trg)
+        out = self.mlp(trg)
         out = self.block3(out, trg)
 
         return out