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authorGustaf Rydholm <gustaf.rydholm@gmail.com>2023-08-25 23:19:14 +0200
committerGustaf Rydholm <gustaf.rydholm@gmail.com>2023-08-25 23:19:14 +0200
commit49ca6ade1a19f7f9c702171537fe4be0dfcda66d (patch)
tree20062ed1910758481f3d5fff11159706c7b990c6 /text_recognizer/network/transformer/embedding/absolute.py
parent0421daf6bd97596703f426ba61c401599b538eeb (diff)
Rename and add flash atten
Diffstat (limited to 'text_recognizer/network/transformer/embedding/absolute.py')
-rw-r--r--text_recognizer/network/transformer/embedding/absolute.py28
1 files changed, 28 insertions, 0 deletions
diff --git a/text_recognizer/network/transformer/embedding/absolute.py b/text_recognizer/network/transformer/embedding/absolute.py
new file mode 100644
index 0000000..08b2c2a
--- /dev/null
+++ b/text_recognizer/network/transformer/embedding/absolute.py
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+from typing import Optional
+
+import torch
+from torch import nn, Tensor
+from text_recognizer.network.transformer.embedding.l2_norm import l2_norm
+
+
+class AbsolutePositionalEmbedding(nn.Module):
+ def __init__(self, dim: int, max_length: int, use_l2: bool = False) -> None:
+ super().__init__()
+ self.scale = dim**-0.5 if not use_l2 else 1.0
+ self.max_length = max_length
+ self.use_l2 = use_l2
+ self.to_embedding = nn.Embedding(max_length, dim)
+ if self.use_l2:
+ nn.init.normal_(self.to_embedding.weight, std=1e-5)
+
+ def forward(self, x: Tensor, pos: Optional[Tensor] = None) -> Tensor:
+ n, device = x.shape[1], x.device
+ assert (
+ n <= self.max_length
+ ), f"Sequence length {n} is greater than the maximum positional embedding {self.max_length}"
+
+ if pos is None:
+ pos = torch.arange(n, device=device)
+
+ embedding = self.to_embedding(pos) * self.scale
+ return l2_norm(embedding) if self.use_l2 else embedding