From 5dd76ca9a3ff35c57cbc7c607afbdb4ee1c8b36f Mon Sep 17 00:00:00 2001
From: Gustaf Rydholm <gustaf.rydholm@gmail.com>
Date: Sat, 3 Sep 2022 00:55:14 +0200
Subject: Add perceiver

---
 text_recognizer/networks/perceiver/attention.py | 48 +++++++++++++++++++++++++
 1 file changed, 48 insertions(+)
 create mode 100644 text_recognizer/networks/perceiver/attention.py

(limited to 'text_recognizer/networks/perceiver/attention.py')

diff --git a/text_recognizer/networks/perceiver/attention.py b/text_recognizer/networks/perceiver/attention.py
new file mode 100644
index 0000000..66aeaa8
--- /dev/null
+++ b/text_recognizer/networks/perceiver/attention.py
@@ -0,0 +1,48 @@
+"""Attention module."""
+from typing import Optional
+
+from einops import rearrange, repeat
+import torch
+from torch import einsum, nn, Tensor
+import torch.nn.functional as F
+
+
+class Attention(nn.Module):
+    def __init__(
+        self,
+        query_dim: int,
+        context_dim: Optional[int] = None,
+        heads: int = 8,
+        dim_head: int = 64,
+    ) -> None:
+        super().__init__()
+        inner_dim = heads * dim_head
+        context_dim = context_dim if context_dim is not None else query_dim
+        self.scale = dim_head ** -0.5
+        self.heads = heads
+
+        self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
+        self.to_kv = nn.Linear(context_dim, 2 * inner_dim, bias=False)
+        self.to_out = nn.Linear(inner_dim, query_dim, bias=False)
+
+    def forward(
+        self, x: Tensor, context: Optional[Tensor] = None, mask=Optional[Tensor]
+    ) -> Tensor:
+        h = self.heads
+        q = self.to_q(x)
+        context = context if context is not None else x
+        k, v = self.to_kv(context).chunk(2, dim=-1)
+
+        q, v, k = map(lambda t: rearrange(t, "b n (h d) -> (b h) n d", h=h), (q, k, v))
+        sim = einsum("b i d, b j d -> b i j", q, k) * self.scale
+
+        if mask is not None:
+            mask = rearrange(mask, "b ... -> b (...)")
+            max_neg_val = -torch.finfo(sim.dtype).max
+            mask = repeat(mask, "b j -> (b h) () j", h=h)
+            sim.masked_fill_(~mask, max_neg_val)
+
+        attn = sim.softmax(dim=-1)
+        out = einsum("b i j, b j d -> b i d", attn, v)
+        out = rearrange(out, "(b h) n d -> b n (h d)", h=h)
+        return self.to_out(out)
-- 
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