From 03c19e0b51e6dbb5a0343e9d1d1bc18c184a164f Mon Sep 17 00:00:00 2001
From: Gustaf Rydholm <gustaf.rydholm@gmail.com>
Date: Mon, 5 Sep 2022 00:03:34 +0200
Subject: Add absolute pos embedding

---
 .../networks/transformer/embeddings/absolute.py    | 34 ++++++++++++++++++++++
 1 file changed, 34 insertions(+)
 create mode 100644 text_recognizer/networks/transformer/embeddings/absolute.py

(limited to 'text_recognizer')

diff --git a/text_recognizer/networks/transformer/embeddings/absolute.py b/text_recognizer/networks/transformer/embeddings/absolute.py
new file mode 100644
index 0000000..e5cdc18
--- /dev/null
+++ b/text_recognizer/networks/transformer/embeddings/absolute.py
@@ -0,0 +1,34 @@
+"""Absolute positional embedding."""
+
+from einops import rearrange
+import torch
+from torch import nn
+import torch.nn.functional as F
+
+
+def l2norm(t, groups=1):
+    t = rearrange(t, "... (g d) -> ... g d", g=groups)
+    t = F.normalize(t, p=2, dim=-1)
+    return rearrange(t, "... g d -> ... (g d)")
+
+
+class AbsolutePositionalEmbedding(nn.Module):
+    def __init__(self, dim, max_seq_len, l2norm_embed=False):
+        super().__init__()
+        self.scale = dim ** -0.5 if not l2norm_embed else 1.0
+        self.max_seq_len = max_seq_len
+        self.l2norm_embed = l2norm_embed
+        self.emb = nn.Embedding(max_seq_len, dim)
+
+    def forward(self, x, pos=None):
+        seq_len = x.shape[1]
+        assert (
+            seq_len <= self.max_seq_len
+        ), f"you are passing in a sequence length of {seq_len} but your absolute positional embedding has a max sequence length of {self.max_seq_len}"
+
+        if pos is None:
+            pos = torch.arange(seq_len, device=x.device)
+
+        pos_emb = self.emb(pos)
+        pos_emb = pos_emb * self.scale
+        return l2norm(pos_emb) if self.l2norm_embed else pos_emb
-- 
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