Remove axial encoder

1 files changed, 0 insertions, 40 deletions

diff --git a/text_recognizer/networks/transformer/axial_attention/self_attention.py b/text_recognizer/networks/transformer/axial_attention/self_attention.py
deleted file mode 100644
index b5e4142..0000000
--- a/text_recognizer/networks/transformer/axial_attention/self_attention.py
+++ /dev/null
@@ -1,40 +0,0 @@
-"""Axial self attention module."""
-
-import torch
-from torch import nn
-from torch import Tensor
-
-
-class SelfAttention(nn.Module):
-    """Axial self attention module."""
-
-    def __init__(
-        self,
-        dim: int,
-        dim_head: int,
-        heads: int,
-    ) -> None:
-        super().__init__()
-        self.dim_hidden = heads * dim_head
-        self.heads = heads
-        self.dim_head = dim_head
-        self.to_q = nn.Linear(dim, self.dim_hidden, bias=False)
-        self.to_kv = nn.Linear(dim, 2 * self.dim_hidden, bias=False)
-        self.to_out = nn.Linear(self.dim_hidden, dim)
-
-    def forward(self, x: Tensor) -> Tensor:
-        """Applies self attention."""
-        q, k, v = (self.to_q(x), *self.to_kv(x).chunk(2, dim=-1))
-        b, _, d, h, e = *q.shape, self.heads, self.dim_head
-
-        merge_heads = (
-            lambda x: x.reshape(b, -1, h, e).transpose(1, 2).reshape(b * h, -1, e)
-        )
-        q, k, v = map(merge_heads, (q, k, v))
-
-        energy = torch.einsum("bie,bje->bij", q, k) * (e ** -0.5)
-        energy = energy.softmax(dim=-1)
-        attn = torch.einsum("bij,bje->bie", energy, v)
-
-        out = attn.reshape(b, h, -1, e).transpose(1, 2).reshape(b, -1, d)
-        return self.to_out(out)