1 files changed, 0 insertions, 79 deletions
diff --git a/text_recognizer/network/convnext/attention.py b/text_recognizer/network/convnext/attention.py
deleted file mode 100644
index 6bc9692..0000000
--- a/text_recognizer/network/convnext/attention.py
+++ /dev/null
@@ -1,79 +0,0 @@
-"""Convolution self attention block."""
-
-import torch.nn.functional as F
-from einops import rearrange
-from torch import Tensor, einsum, nn
-
-from text_recognizer.network.convnext.norm import LayerNorm
-from text_recognizer.network.convnext.residual import Residual
-
-
-def l2norm(t: Tensor) -> Tensor:
-    return F.normalize(t, dim=-1)
-
-
-class FeedForward(nn.Module):
-    def __init__(self, dim: int, mult: int = 4) -> None:
-        super().__init__()
-        inner_dim = int(dim * mult)
-        self.fn = Residual(
-            nn.Sequential(
-                LayerNorm(dim),
-                nn.Conv2d(dim, inner_dim, 1, bias=False),
-                nn.GELU(),
-                LayerNorm(inner_dim),
-                nn.Conv2d(inner_dim, dim, 1, bias=False),
-            )
-        )
-
-    def forward(self, x: Tensor) -> Tensor:
-        return self.fn(x)
-
-
-class Attention(nn.Module):
-    def __init__(
-        self, dim: int, heads: int = 4, dim_head: int = 64, scale: int = 8
-    ) -> None:
-        super().__init__()
-        self.scale = scale
-        self.heads = heads
-        inner_dim = heads * dim_head
-        self.norm = LayerNorm(dim)
-
-        self.to_qkv = nn.Conv2d(dim, inner_dim * 3, 1, bias=False)
-        self.to_out = nn.Conv2d(inner_dim, dim, 1, bias=False)
-
-    def forward(self, x: Tensor) -> Tensor:
-        h, w = x.shape[-2:]
-
-        residual = x.clone()
-
-        x = self.norm(x)
-
-        q, k, v = self.to_qkv(x).chunk(3, dim=1)
-        q, k, v = map(
-            lambda t: rearrange(t, "b (h c) ... -> b h (...) c", h=self.heads),
-            (q, k, v),
-        )
-
-        q, k = map(l2norm, (q, k))
-
-        sim = einsum("b h i d, b h j d -> b h i j", q, k) * self.scale
-        attn = sim.softmax(dim=-1)
-
-        out = einsum("b h i j, b h j d -> b h i d", attn, v)
-
-        out = rearrange(out, "b h (x y) d -> b (h d) x y", x=h, y=w)
-        return self.to_out(out) + residual
-
-
-class TransformerBlock(nn.Module):
-    def __init__(self, attn: Attention, ff: FeedForward) -> None:
-        super().__init__()
-        self.attn = attn
-        self.ff = ff
-
-    def forward(self, x: Tensor) -> Tensor:
-        x = self.attn(x)
-        x = self.ff(x)
-        return x