From fc7fb0df5aa704aab3d73eab964631c8be924c42 Mon Sep 17 00:00:00 2001
From: Gustaf Rydholm <gustaf.rydholm@gmail.com>
Date: Mon, 5 Sep 2022 00:04:25 +0200
Subject: Update norm

---
 text_recognizer/networks/transformer/norm.py | 16 ++++++++++++++--
 1 file changed, 14 insertions(+), 2 deletions(-)

(limited to 'text_recognizer/networks/transformer')

diff --git a/text_recognizer/networks/transformer/norm.py b/text_recognizer/networks/transformer/norm.py
index 537246d..4cd3b5b 100644
--- a/text_recognizer/networks/transformer/norm.py
+++ b/text_recognizer/networks/transformer/norm.py
@@ -4,7 +4,7 @@ Copied from lucidrains:
     https://github.com/lucidrains/x-transformers/blob/main/x_transformers/x_transformers.py
 
 """
-from typing import Dict, Type
+from typing import Dict, Optional, Type
 
 import torch
 from torch import nn
@@ -29,12 +29,24 @@ class RMSNorm(nn.Module):
 class PreNorm(nn.Module):
     """Applies layer normalization then function."""
 
-    def __init__(self, normalized_shape: int, fn: Type[nn.Module]) -> None:
+    def __init__(
+        self,
+        normalized_shape: int,
+        fn: Type[nn.Module],
+        context_dim: Optional[int] = None,
+    ) -> None:
         super().__init__()
         self.norm = nn.LayerNorm(normalized_shape)
         self.fn = fn
+        self.norm_context = (
+            nn.LayerNorm(context_dim) if context_dim is not None else None
+        )
 
     def forward(self, x: Tensor, **kwargs) -> Tensor:
         """Applies pre norm."""
         x = self.norm(x)
+        if self.norm_context is not None:
+            context = kwargs["context"]
+            normed_context = self.norm_context(context)
+            kwargs.update(context=normed_context)
         return self.fn(x, **kwargs)
-- 
cgit v1.2.3-70-g09d2